diff --git a/.github/workflows/ai_pr_review.yml b/.github/workflows/ai_pr_review.yml index 984aafca..344c12db 100644 --- a/.github/workflows/ai_pr_review.yml +++ b/.github/workflows/ai_pr_review.yml @@ -340,9 +340,13 @@ jobs: echo "Unified diff (context=5):" # Exclude large generated/data files from the full diff to stay # within the model's input limit. The --name-status above still - # lists them. Narrowed to real-data assets and notebook outputs. + # lists them. Narrowed to real-data assets, notebook outputs, and + # the qte golden fixture (generated single-line JSON, ~700KB - a + # regeneration blew the input limit at PR #688; the reviewable + # artifact is benchmarks/R/generate_qte_golden.R). git --no-pager diff --unified=5 "$BASE_SHA" "$HEAD_SHA" \ -- . ':!benchmarks/data/real/*.json' ':!benchmarks/data/real/*.csv' \ + ':!benchmarks/data/qte_golden.json' \ ':!docs/tutorials/*.ipynb' } >> "$PROMPT" diff --git a/CHANGELOG.md b/CHANGELOG.md index f5137ca9..7f526d5c 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -83,7 +83,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 DiD for the canonical 2x2 design with continuous outcomes: `ChangesInChanges` (alias `CiC`) recovers the treated group's full counterfactual outcome distribution and quantile treatment effects via the CDF transformation - `F_10(F_00^{-1}(F_01(y)))`, invariant to monotone outcome rescaling; `QDiD` + `F_10(F_00^{-1}(F_01(y)))`, invariant to monotone outcome rescaling + (unconditional fits; the covariate QR branch is not); `QDiD` is the paper's additive quantile-DiD comparison estimator. Point estimation matches R `qte` 1.3.1 exactly (CiC via an exact port of R's type-1 quantile arithmetic incl. its fuzz handling - bit-exact golden parity; QDiD via qte's @@ -91,12 +92,27 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 paper's transformation, population-equivalent). Bootstrap-only inference (panel unit-block or pooled repeated-cross-section resampling per qte's schemes; seedable, `n_bootstrap=200` default; replicate-SD SEs, normal - intervals, sup-t uniform bands at qte's fixed 95% level). Diagnostics: - interior-range guard (eq. 17) with NaN inference outside, support-violation / - heavy-ties / QDiD-non-monotonicity warnings. Deferred and documented: - covariates, discrete-outcome bounds, analytical SEs, staggered designs. - Golden fixtures: `benchmarks/R/generate_qte_golden.R` (qte pinned 1.3.1) -> - `benchmarks/data/qte_golden.json`. + intervals, sup-t uniform bands at qte's fixed 95% level). Covariates are + supported on both estimators (`covariates=[...]` or trailing formula terms), + porting qte's `xformla` branch exactly: per-cell linear quantile regressions + (Koenker-Bassett LP via scipy HiGHS) on qte's fixed internal 0.01-0.99 tau + grid, quantreg `predict.rqs` Fhat/Qhat step-function conventions verbatim, + per-observation conditional-rank imputation, QR refits inside every + bootstrap replicate, and a conditional-envelope support diagnostic + (Melly-Santangelo 2015, Assumption 4); the unconditional interior-range + guard applies to covariate-free fits only. Diagnostics: interior-range + guard (eq. 17) with NaN inference outside, support-violation / heavy-ties / + QDiD-non-monotonicity warnings (the latter two scoped as documented in + REGISTRY.md under covariates). Deferred and documented: the full + Melly-Santangelo covariate estimator, discrete-outcome bounds, analytical + SEs, staggered designs. Golden fixtures: + `benchmarks/R/generate_qte_golden.R` (qte pinned 1.3.1, quantreg 6.1) -> + `benchmarks/data/qte_golden.json`, with covariate scenarios, atol=0 + convention micro-fixtures (`qr_cases`), and deterministic seeded SE blocks + (`cores=1`; qte's default forked bootstrap is not reproducible even seeded). + Cross-language covariate parity is layered - conventions bit-exact given R + inputs, LP solver proven optimal per tau, end-to-end results gated at + documented exact-tie selection bounds (see the REGISTRY Deviation note). ### Changed - `diff_diff/guides/llms-autonomous.txt` no longer lists regression discontinuity as diff --git a/ROADMAP.md b/ROADMAP.md index bae652f1..8e28cebe 100644 --- a/ROADMAP.md +++ b/ROADMAP.md @@ -84,12 +84,6 @@ Extends DiD to duration / survival outcomes where standard methods fail (hazard **Reference**: Deaner & Ku (2025), *AEA Conference Paper*. -### Quantile / Distributional DiD - -Recover the full counterfactual distribution and quantile treatment effects (QTT), not just mean ATT. Changes-in-Changes (CiC) identification strategy. - -**Reference**: Athey & Imbens (2006), *Econometrica*. (Ciaccio 2024 extension listed under Under Consideration.) - ### CATT Meta-Learner for Heterogeneous Effects ML-powered conditional ATT, using a doubly robust meta-learner to discover which units benefit most from treatment. diff --git a/TODO.md b/TODO.md index f914a0c9..a9fea06f 100644 --- a/TODO.md +++ b/TODO.md @@ -41,6 +41,7 @@ generic sparse-FE, QR+SVD rank-detection redundancy, `check_finite` bypass — m | Issue | Location | Origin | Effort | Priority | |-------|----------|--------|--------|----------| | `EfficientDiD` conditional path: the largest remaining O(n) stage is the sieve/nuisance construction outside the tiled pass (~9s at 10k). (The `_ridge_solve_weights` Python-prep shave landed 2026-07-07 — the `omega_stack[rest]` fancy-index copy and tail scatter are skipped when no row is zero-masked, byte-identical outputs; the `zero_mask` abs scan itself remains, needed for correctness.) | `efficient_did_covariates.py` | CS-scaling | Mid | Low | +| `_rq_fit` LP assembly is dense (`A_eq = [X, I, -I]` with dense identity blocks, rebuilt per cell fit): a `scipy.sparse` construction would cut memory and likely HiGHS time for large cells / bootstrap-heavy covariate CiC/QDiD fits. CAVEAT before doing it: a different matrix representation can change HiGHS's vertex selection at degenerate/tied QR optima - end-to-end covariate goldens are tie-selection-gated (fine), but the `qr_cases` tight coefficient matches may shift to the equal-loss branch; re-run the parity suite and re-calibrate if needed. | `diff_diff/changes_in_changes.py::_rq_fit` | covariates PR | Quick | Low | | Evaluate flipping `DIFF_DIFF_SOLVE_OLS_FASTPATH` default-ON after an opt-in soak (the 2026-07 certified normal-equations Cholesky fast path, both backends). A flip needs: golden/parity-suite recapture at the tol-bounded posture (fitted ~1e-8 abs / SE ~1e-6 rel — the default today is byte-pinned in several benchmark conventions), certification-rate telemetry across real workloads (any decline is silent-correct but forfeits the speedup), and the staged default-flip protocol used for `df_convention` (v4-class change). | `diff_diff/linalg.py::_resolve_solve_ols_fastpath`, `rust/src/linalg.rs::solve_ols_chol` | CS-scaling | Mid | Low | ### Testing / docs @@ -112,7 +113,7 @@ Doable in principle, but no current caller and/or explicitly out of paper scope. | Issue | Location | PR | Priority | |-------|----------|----|----------| -| ChangesInChanges covariates (Melly-Santangelo 2015 QR pipeline: per-cell quantile-regression conditional CDFs -> conditional CiC -> integrate over treated-group covariates). No R parity target exists (the MS Stata code is the only implementation; distinct from Kranker's `cic`); would need simulation-based validation. Reviewed: `docs/methodology/papers/melly-santangelo-2015-review.md`. | `diff_diff/changes_in_changes.py` | #682 | Low | +| ChangesInChanges FULL Melly-Santangelo covariate estimator (monotonized integrated-indicator conditional CDFs, treated-post `F_{X|11}` integration, exchangeable bootstrap with variance-weighted KS bands, tail trimming, pre-period specification test). The qte-`xformla` simplified form of the MS pipeline SHIPPED in the covariates PR (`covariates=` on both estimators, parity-tested vs qte 1.3.1); this row's earlier "No R parity target exists" claim was WRONG and is corrected in the MS review doc. The full estimator has no reference implementation (the MS Stata code is the only one; distinct from Kranker's `cic`) and would need simulation-based validation. Reviewed: `docs/methodology/papers/melly-santangelo-2015-review.md`. | `diff_diff/changes_in_changes.py` | #682 | Low | | ChangesInChanges discrete-outcome bounds + DCIC point identification (Athey-Imbens Sections 4/5.2 incl. Imbens-Manski intervals; Kranker's Stata `cic` is the reference). The shipped ties warning marks the boundary of the continuous scope. | `diff_diff/changes_in_changes.py` | #682 | Low | | ChangesInChanges analytical SEs (Athey-Imbens Theorems 5.1-5.3 influence functions, panel 5.5-5.7, Appendix B covariances; needs the footnote-31 boundary density estimator - note the review's suspected half-range/midpoint typo). Bootstrap is the shipped inference. | `diff_diff/changes_in_changes.py` | #682 | Low | | Staggered/multi-period distributional DiD (Athey-Imbens Section 6 / Ciaccio arXiv:2408.01208v2; `ecic` is the staggered event-study CiC lineage - a distinct method from Ciaccio's copula approach, do not conflate). Reviewed: `docs/methodology/papers/ciaccio-2024-review.md`; ROADMAP row is reviewed-deferred pending demand. | `diff_diff/changes_in_changes.py` | #682 | Low | diff --git a/benchmarks/R/generate_qte_golden.R b/benchmarks/R/generate_qte_golden.R index d251ef64..44365c62 100644 --- a/benchmarks/R/generate_qte_golden.R +++ b/benchmarks/R/generate_qte_golden.R @@ -7,23 +7,34 @@ # Output: benchmarks/data/qte_golden.json, consumed by # tests/test_changes_in_changes_parity.py (which pytest.skips when absent). # -# Version contract: qte is pinned to 1.3.1 here, in benchmarks/R/requirements.R -# (pinned_versions), and in the parity test's test_metadata_versions_match. -# Bump all three in lockstep when re-anchoring. +# Version contract: qte is pinned to 1.3.1 and quantreg to 6.1 here, in +# benchmarks/R/requirements.R (pinned_versions), and in the parity test's +# test_metadata_versions_match. Bump all in lockstep when re-anchoring. +# quantreg is part of the contract because the covariate (xformla) fixtures +# embed quantreg's rq / predict.rqs behavior, not just qte's. # -# Point fixtures are generated with se=FALSE so they are fully deterministic -# (qte's bootstrap is not seedable through its public API). The SE block runs -# seeded (set.seed before each call) with iters=999; Python compares those SEs -# statistically, not bit-exactly. +# Point fixtures are generated with se=FALSE so they are fully deterministic. +# The SE block runs seeded (set.seed before each call) with iters=999 AND +# cores=1: qte's default cores=2 parallelizes the bootstrap via forking, whose +# per-child RNG seeding is NOT reproducible even under set.seed (verified +# empirically 2026-07-13 - back-to-back seeded runs differ). With cores=1 the +# draw sequence is fully deterministic. Python still compares these SEs +# statistically, not bit-exactly (the R draw sequence cannot be replicated +# cross-language). suppressMessages({ library(qte) + library(quantreg) library(jsonlite) }) stopifnot(packageVersion("qte") == "1.3.1") +stopifnot(packageVersion("quantreg") == "6.1") probs <- seq(0.05, 0.95, 0.05) +# qte's covariate branch hardcodes this 99-tau quantile-regression grid inside +# compute.CiC / compute.QDiD; stored in metadata so Python pins its own copy. +qr_taus <- seq(0.01, 0.99, 0.01) # --------------------------------------------------------------------------- # Synthetic DGPs (long format: id, period 0/1, treat 0/1 group indicator, y) @@ -65,6 +76,57 @@ make_lognormal_2x2 <- function(n_treated, n_control, seed) { ) } +make_cov1_2x2 <- function(n_treated, n_control, seed) { + # One covariate: composition differs by group, and both the time trend and + # the noise scale depend on x, so covariate adjustment matters and CiC/QDiD + # differ. x is time-invariant; continuous throughout (the conditional-rank + # pipeline is knife-edge on outcomes that sit exactly on predicted knots, so + # covariate fixtures avoid mass points entirely). + set.seed(seed) + n <- n_treated + n_control + treat <- c(rep(1L, n_treated), rep(0L, n_control)) + x <- runif(n, 0, 2) + 0.4 * treat + u <- rnorm(n) + y_pre <- 0.5 + 0.8 * x + (0.4 + 0.2 * x) * u + y_post <- 0.8 + 1.1 * x + (0.5 + 0.2 * x) * (0.7 * u + 0.5 * rnorm(n)) + + treat * (0.6 + 0.3 * pnorm(u)) + data.frame( + id = rep(seq_len(n), times = 2), + period = rep(c(0L, 1L), each = n), + treat = rep(treat, times = 2), + x1 = rep(x, times = 2), + y = c(y_pre, y_post) + ) +} + +make_cov2_2x2 <- function(n_treated, n_control, seed) { + # Two covariates: continuous x1 plus a binary x2 stored as 0/1 NUMERIC + # (exercises multi-column quantile regression; the fixture never uses R + # factors so the R and Python design matrices are identical by construction). + set.seed(seed) + n <- n_treated + n_control + treat <- c(rep(1L, n_treated), rep(0L, n_control)) + x1 <- rnorm(n, mean = 1, sd = 0.5) + 0.3 * treat + x2 <- as.numeric(runif(n) < 0.4 + 0.2 * treat) + u <- rnorm(n) + y_pre <- 0.2 + 0.6 * x1 - 0.4 * x2 + (0.5 + 0.15 * x1) * u + y_post <- 0.5 + 0.9 * x1 - 0.2 * x2 + (0.5 + 0.15 * x1) * (0.6 * u + 0.6 * rnorm(n)) + + treat * (0.5 + 0.4 * pnorm(u)) + df <- data.frame( + id = rep(seq_len(n), times = 2), + period = rep(c(0L, 1L), each = n), + treat = rep(treat, times = 2), + x1 = rep(x1, times = 2), + x2 = rep(x2, times = 2), + y = c(y_pre, y_post) + ) + # Guard against a degenerate draw: x2 must vary within every (g, t) cell. + for (gv in 0:1) for (tv in 0:1) { + stopifnot(var(df$x2[df$treat == gv & df$period == tv]) > 0) + } + df +} + dgps <- list( normal_2x2_n500 = make_normal_2x2(250, 250, seed = 20260712), lognormal_2x2_n300 = make_lognormal_2x2(150, 150, seed = 20260713), @@ -72,6 +134,40 @@ dgps <- list( smalln_2x2_n60 = make_normal_2x2(35, 25, seed = 20260714) ) +# Covariate scenarios (qte xformla branch). Kept separate from `dgps`: their +# result keys and data blocks carry covariate columns. +# +# WHY END-TO-END COVARIATE PARITY IS TIE-SELECTION-BOUNDED (not ~1e-10 like +# the unconditional fixtures). Two exact-tie artifacts make bit-level +# cross-language agreement unattainable for the QR pipeline: +# (i) Optimal-face vertex selection: the QR check loss can have a +# non-trivial optimal face (structural with binary covariates; data- +# dependent otherwise). R's br simplex and Python's HiGHS then return +# DIFFERENT exact minimizers (equal loss to ~1e-15, coefficients apart +# by O(1e-1)). +# (ii) Fhat knot-tie ordering: adjacent taus often share an interpolating +# basis, so R's predicted knots are EXACTLY tied and predict.rqs's +# stable sort orders them by tau index. Python coefficients agree only +# to ~1e-15, which breaks those ties in arbitrary value order, so +# ~5-10% of conditional ranks legitimately land a grid step away. +# Both are selections among equally valid solutions. The parity suite +# therefore pins the CONVENTIONS at atol=0 conditioned on R's stored +# coefficients/predictions (qr_cases below), proves the LP solver optimal +# per tau (equal coefficients OR equal loss), and gates end-to-end results +# at empirically measured tie-selection bounds (metadata cov_*_atol). +# Cell sizes are kept coprime to 100 (integer n*tau is one avoidable +# degeneracy source); data are continuous throughout. +cov_specs <- list( + cov1_2x2_n300 = list( + df = make_cov1_2x2(151, 149, seed = 20260716), + xformla = ~x1, xcols = c("x1") + ), + cov2_2x2_n240 = list( + df = make_cov2_2x2(123, 117, seed = 20260717), + xformla = ~ x1 + x2, xcols = c("x1", "x2") + ) +) + # lalonde (qte-shipped): 1975/1978 two-period panel; re78 zeros give heavy ties. data(lalonde, package = "qte") lal <- lalonde.psid.panel[lalonde.psid.panel$year %in% c(1975, 1978), ] @@ -86,10 +182,11 @@ dgps$lalonde_psid <- data.frame( # Point fixtures: {CiC, QDiD} x {panel, repeated cross-section}, se = FALSE # --------------------------------------------------------------------------- -run_point <- function(df, method, panel) { +run_point <- function(df, method, panel, xformla = NULL) { fn <- if (method == "cic") CiC else QDiD res <- fn( formla = y ~ treat, + xformla = xformla, t = 1, tmin1 = 0, tname = "period", data = df, panel = panel, @@ -124,20 +221,51 @@ for (dgp_name in names(dgps)) { scenarios[[dgp_name]] <- entry } +for (cov_name in names(cov_specs)) { + spec <- cov_specs[[cov_name]] + df <- spec$df + data_block <- list( + id = df$id, + period = df$period, + treat = df$treat, + y = df$y + ) + for (cn in spec$xcols) data_block[[cn]] <- df[[cn]] + entry <- list( + data = data_block, + covariates = spec$xcols, + results = list() + ) + for (method in c("cic", "qdid")) { + for (panel in c(TRUE, FALSE)) { + key <- sprintf("%s_cov_%s", method, if (panel) "panel" else "rcs") + entry$results[[key]] <- run_point(df, method, panel, xformla = spec$xformla) + message(sprintf( + "%s / %s: ate = %.10g", cov_name, key, entry$results[[key]]$ate + )) + } + } + scenarios[[cov_name]] <- entry +} + # --------------------------------------------------------------------------- # SE block (statistical parity): normal_2x2_n500 only, seeded, iters = 999 # --------------------------------------------------------------------------- -run_se <- function(df, method, panel, seed) { +run_se <- function(df, method, panel, seed, xformla = NULL) { fn <- if (method == "cic") CiC else QDiD set.seed(seed) res <- fn( formla = y ~ treat, + xformla = xformla, t = 1, tmin1 = 0, tname = "period", data = df, panel = panel, idname = if (panel) "id" else NULL, se = TRUE, iters = 999, + # cores = 1 is REQUIRED for reproducibility: qte's default cores = 2 + # forks the bootstrap and the per-child RNG seeding ignores set.seed. + cores = 1, probs = probs ) list( @@ -151,8 +279,16 @@ se_block <- list( cic_panel = run_se(dgps$normal_2x2_n500, "cic", TRUE, seed = 42), cic_rcs = run_se(dgps$normal_2x2_n500, "cic", FALSE, seed = 42), qdid_panel = run_se(dgps$normal_2x2_n500, "qdid", TRUE, seed = 42), - qdid_rcs = run_se(dgps$normal_2x2_n500, "qdid", FALSE, seed = 42) + qdid_rcs = run_se(dgps$normal_2x2_n500, "qdid", FALSE, seed = 42), + # Covariate SE parity: one panel CiC + one RCS QDiD block on the 1-covariate + # scenario (data lives under scenarios$cov1_2x2_n300). + cic_cov_panel = run_se(cov_specs$cov1_2x2_n300$df, "cic", TRUE, seed = 42, xformla = ~x1), + qdid_cov_rcs = run_se(cov_specs$cov1_2x2_n300$df, "qdid", FALSE, seed = 42, xformla = ~x1) ) +se_block$cic_cov_panel$covariates <- c("x1") +se_block$cic_cov_panel$scenario <- "cov1_2x2_n300" +se_block$qdid_cov_rcs$covariates <- c("x1") +se_block$qdid_cov_rcs$scenario <- "cov1_2x2_n300" message("SE block done.") # --------------------------------------------------------------------------- @@ -187,6 +323,87 @@ micro <- list( n101_near_integer = micro_case(x101, pmin(pmax(p_near_int101, 0), 1)) ) +# --------------------------------------------------------------------------- +# QR micro-fixtures (covariate convention anchor): per-cell rq coefficients, +# prediction matrices, and the exact predict.rqs Fhat/Qhat outputs qte's +# covariate branch consumes - so Python can pin the step-function conventions +# at atol=0 given R's coefficients (isolating convention-port errors from +# LP-solver last-digit differences), and the LP solver separately. +# --------------------------------------------------------------------------- + +qr_case <- function(df, xcols, xformla) { + cells <- list( + c00 = df[df$treat == 0 & df$period == 0, ], + c01 = df[df$treat == 0 & df$period == 1, ], + c10 = df[df$treat == 1 & df$period == 0, ], + c11 = df[df$treat == 1 & df$period == 1, ] + ) + yformla <- as.formula(paste("y ~", paste(xcols, collapse = " + "))) + QR00 <- rq(yformla, data = cells$c00, tau = qr_taus) + QR01 <- rq(yformla, data = cells$c01, tau = qr_taus) + QR10 <- rq(yformla, data = cells$c10, tau = qr_taus) + + # Prediction matrices at the treated-pre covariates (n10 x 99): separates + # dot-product-ulp failures from step-function-convention failures. + X10d <- cbind(1, as.matrix(cells$c10[, xcols, drop = FALSE])) + preds00 <- unname(X10d %*% coef(QR00)) + preds01 <- unname(X10d %*% coef(QR01)) + preds10 <- unname(X10d %*% coef(QR10)) + + n10 <- nrow(cells$c10) + # CiC composition (mirrors qte compute.CiC's xformla branch). + F00 <- predict(QR00, newdata = cells$c10, type = "Fhat", stepfun = TRUE) + cic_ranks <- sapply(seq_len(n10), function(i) F00[[i]](cells$c10$y[i])) + Q01 <- predict(QR01, newdata = cells$c10, type = "Qhat", stepfun = TRUE) + cic_y0t <- sapply(seq_len(n10), function(i) Q01[[i]](cic_ranks[i])) + # QDiD composition (mirrors qte compute.QDiD's xformla branch). + F10 <- predict(QR10, newdata = cells$c10, type = "Fhat", stepfun = TRUE) + qdid_ranks <- sapply(seq_len(n10), function(i) F10[[i]](cells$c10$y[i])) + Q00 <- predict(QR00, newdata = cells$c10, type = "Qhat", stepfun = TRUE) + qdid_y0t <- cells$c10$y + + sapply(seq_len(n10), function(i) Q01[[i]](qdid_ranks[i])) - + sapply(seq_len(n10), function(i) Q00[[i]](qdid_ranks[i])) + + # End-to-end micro results from the actual qte calls (RCS mode): guards + # against qte's internals diverging from raw predict.rqs usage. + cic_res <- CiC(y ~ treat, xformla = xformla, t = 1, tmin1 = 0, tname = "period", + data = df, panel = FALSE, se = FALSE, probs = probs) + qdid_res <- QDiD(y ~ treat, xformla = xformla, t = 1, tmin1 = 0, tname = "period", + data = df, panel = FALSE, se = FALSE, probs = probs) + + cell_block <- lapply(cells, function(cc) { + b <- list(y = cc$y) + for (cn in xcols) b[[cn]] <- cc[[cn]] + b + }) + list( + covariates = xcols, + cells = cell_block, + coef00 = unname(as.matrix(coef(QR00))), # (k+1) x 99, intercept row first + coef01 = unname(as.matrix(coef(QR01))), + coef10 = unname(as.matrix(coef(QR10))), + preds00_at_x10 = preds00, + preds01_at_x10 = preds01, + preds10_at_x10 = preds10, + cic_ranks = cic_ranks, + cic_y0t = cic_y0t, + qdid_ranks = qdid_ranks, + qdid_y0t = qdid_y0t, + cic_ate = cic_res$ate, cic_qte = as.numeric(cic_res$qte), + qdid_ate = qdid_res$ate, qdid_qte = as.numeric(qdid_res$qte) + ) +} + +# Micro cells sized coprime to 100 too (see the cov_specs comment). The +# qr1 case (continuous covariate) has unique QR optima, so _rq_fit matches +# R's coefficients directly; qr2's binary covariate makes optimal faces +# structural, exercising the equal-loss branch of the solver test. +qr_cases <- list( + qr1_cov1_n41cell = qr_case(make_cov1_2x2(41, 41, seed = 20260718), c("x1"), ~x1), + qr2_cov2_n33cell = qr_case(make_cov2_2x2(33, 29, seed = 20260719), c("x1", "x2"), ~ x1 + x2) +) +message("QR micro-fixtures done.") + # --------------------------------------------------------------------------- # Write JSON # --------------------------------------------------------------------------- @@ -196,17 +413,31 @@ out <- list( description = paste( "Golden fixtures for diff-diff ChangesInChanges/QDiD parity vs qte.", "Point fixtures use se=FALSE (deterministic); the se_block is seeded", - "(set.seed before each call, iters=999) and compared statistically." + "(set.seed before each call, iters=999) and compared statistically.", + "Covariate (xformla) fixtures also embed quantreg rq/predict.rqs", + "behavior; qr_cases anchors those conventions at atol=0." ), qte_version = as.character(packageVersion("qte")), + quantreg_version = as.character(packageVersion("quantreg")), r_version = as.character(getRversion()), probs = probs, + qr_taus = qr_taus, point_atol = 1e-10, + # Covariate end-to-end gates: tie-selection bounds (see the cov_specs + # comment). The committed scenarios measure worst att 9.3e-3 / qte 8.1e-2 + # on the generation platform; a 30-dataset randomized decomposition audit + # (2026-07-13) observed same-mechanism deviations up to 3.5e-2 / 0.40 on + # adversarial (binary-covariate / ties-heavy / small-cell) data, so the + # gates carry cross-platform tie-flip margin. The exact conventions are + # pinned at atol=0 by qr_cases instead. + cov_att_atol = 0.04, + cov_qte_atol = 0.25, n_scenarios = length(scenarios) ), scenarios = scenarios, se_block = se_block, - quantile_cases = micro + quantile_cases = micro, + qr_cases = qr_cases ) out_path <- file.path("benchmarks", "data", "qte_golden.json") diff --git a/benchmarks/R/requirements.R b/benchmarks/R/requirements.R index c20628b1..7e3cb869 100644 --- a/benchmarks/R/requirements.R +++ b/benchmarks/R/requirements.R @@ -73,8 +73,11 @@ pinned_versions <- list( DIDHAD = "2.0.0", YatchewTest = "1.1.1", nprobust = "0.5.0", - # CiC/QDiD R-parity (generate_qte_golden.R + tests/test_changes_in_changes_parity.py) - qte = "1.3.1" + # CiC/QDiD R-parity (generate_qte_golden.R + tests/test_changes_in_changes_parity.py). + # quantreg is pinned too: the covariate (xformla) golden fixtures embed + # quantreg's rq/predict.rqs behavior, not just qte's. + qte = "1.3.1", + quantreg = "6.1" ) install_github_if_missing <- function(pkg, repo) { diff --git a/benchmarks/data/qte_golden.json b/benchmarks/data/qte_golden.json index 238387e3..8dde5d2f 100644 --- a/benchmarks/data/qte_golden.json +++ b/benchmarks/data/qte_golden.json @@ -1 +1 @@ -{"metadata":{"description":"Golden fixtures for diff-diff ChangesInChanges/QDiD parity vs qte. Point fixtures use se=FALSE (deterministic); the se_block is seeded (set.seed before each call, iters=999) and compared 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+{"metadata":{"description":"Golden fixtures for diff-diff ChangesInChanges/QDiD parity vs qte. Point fixtures use se=FALSE (deterministic); the se_block is seeded (set.seed before each call, iters=999) and compared statistically. Covariate (xformla) fixtures also embed quantreg rq/predict.rqs behavior; qr_cases anchors those conventions at 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diff --git a/diff_diff/changes_in_changes.py b/diff_diff/changes_in_changes.py index fe53e1f8..a93202ce 100644 --- a/diff_diff/changes_in_changes.py +++ b/diff_diff/changes_in_changes.py @@ -27,12 +27,22 @@ variance is deferred): panel mode resamples units (both periods travel together); repeated cross-section mode draws a single pooled row resample. SEs are SDs over replicates with symmetric normal-approximation intervals. - -Scope (v1, per docs/methodology/papers/athey-imbens-2006-review.md): 2x2 -design, continuous outcomes, no covariates. Deferred and documented in the -methodology registry: covariates (Melly-Santangelo 2015), discrete-outcome -bounds (Section 4), analytical standard errors (Theorems 5.1-5.7), multiple -groups/periods (Section 6), and treatment-on-the-controls (Theorem 3.2). +- Covariates (``covariates=`` or trailing formula terms) port qte's ``xformla`` + branch exactly - the Melly-Santangelo (2015) quantile-regression pipeline in + qte's simplified form: per-cell linear quantile regressions on the fixed + 99-tau grid ``seq(0.01, 0.99, 0.01)``, quantreg ``predict.rqs`` + ``Fhat``/``Qhat`` step-function conventions (verbatim, including their + boundary behavior; no rearrangement), per-observation conditional-rank + imputation integrated over the treated pre-period covariate distribution, + and quantile regressions refit inside every bootstrap replicate. + +Scope (per docs/methodology/papers/athey-imbens-2006-review.md): 2x2 design, +continuous outcomes, numeric covariates. Deferred and documented in the +methodology registry: the full Melly-Santangelo (2015) covariate estimator +(monotonized integrated-indicator CDFs, treated-post covariate integration, +exchangeable bootstrap), discrete-outcome bounds (Section 4), analytical +standard errors (Theorems 5.1-5.7), multiple groups/periods (Section 6), and +treatment-on-the-controls (Theorem 3.2). """ import warnings @@ -41,10 +51,16 @@ import numpy as np import pandas as pd from scipy import stats +from scipy.optimize import linprog from diff_diff.bootstrap_utils import warn_bootstrap_failure_rate from diff_diff.changes_in_changes_results import ChangesInChangesResults -from diff_diff.utils import safe_inference, safe_inference_batch, validate_binary +from diff_diff.utils import ( + safe_inference, + safe_inference_batch, + validate_binary, + validate_covariate_names, +) # Default quantile grid: qte's ``probs = seq(0.05, 0.95, 0.05)`` (19 points), pinned to # R's EXACT seq() doubles. ``np.arange(0.05, 0.96, 0.05)`` differs from R at 5 of the 19 @@ -75,12 +91,130 @@ ] ) +# Covariate-path quantile-regression tau grid: qte hardcodes ``seq(0.01, 0.99, 0.01)`` +# inside compute.CiC/compute.QDiD (99 taus, not user-configurable). Pinned to R's EXACT +# seq() doubles for the same reason as _DEFAULT_QUANTILES: natural numpy constructions +# differ at 15-25 of the 99 indices by one ulp, the Fhat rank values are drawn FROM this +# grid, and the Qhat step function has its knots ON it - exact-knot searchsorted +# comparisons are ulp-sensitive. Locked against the golden fixture's stored qr_taus by +# tests/test_changes_in_changes_parity.py. +_QR_TAU_GRID = np.array( + [ + 0.01, + 0.02, + 0.03, + 0.04, + 0.05, + 0.060000000000000005, + 0.06999999999999999, + 0.08, + 0.09, + 0.09999999999999999, + 0.11, + 0.12, + 0.13, + 0.14, + 0.15000000000000002, + 0.16, + 0.17, + 0.18000000000000002, + 0.19, + 0.2, + 0.21000000000000002, + 0.22, + 0.23, + 0.24000000000000002, + 0.25, + 0.26, + 0.27, + 0.28, + 0.29000000000000004, + 0.3, + 0.31, + 0.32, + 0.33, + 0.34, + 0.35000000000000003, + 0.36000000000000004, + 0.37, + 0.38, + 0.39, + 0.4, + 0.41000000000000003, + 0.42000000000000004, + 0.43, + 0.44, + 0.45, + 0.46, + 0.47000000000000003, + 0.48000000000000004, + 0.49, + 0.5, + 0.51, + 0.52, + 0.53, + 0.54, + 0.55, + 0.56, + 0.5700000000000001, + 0.5800000000000001, + 0.59, + 0.6, + 0.61, + 0.62, + 0.63, + 0.64, + 0.65, + 0.66, + 0.67, + 0.68, + 0.6900000000000001, + 0.7000000000000001, + 0.7100000000000001, + 0.72, + 0.73, + 0.74, + 0.75, + 0.76, + 0.77, + 0.78, + 0.79, + 0.8, + 0.81, + 0.8200000000000001, + 0.8300000000000001, + 0.8400000000000001, + 0.85, + 0.86, + 0.87, + 0.88, + 0.89, + 0.9, + 0.91, + 0.92, + 0.93, + 0.9400000000000001, + 0.9500000000000001, + 0.9600000000000001, + 0.97, + 0.98, + 0.99, + ] +) + # Duplicate-share threshold above which the discrete-outcome warning fires. Library # choice: the paper's continuous machinery (Assumption 5.1(iii)) has no finite-sample # ties rule, and applying it to discrete data silently returns one endpoint of the # Section 4 bounds rather than a point estimate. _TIE_SHARE_WARN = 0.10 +# Share of treated pre-period observations outside their conditional quantile envelope +# (the span of the 99 predicted per-observation quantiles) above which the covariate +# support warning fires. ~2% outside is EXPECTED under correct specification - the +# envelope only spans taus 0.01-0.99 - so the threshold sits well above that; 10% +# signals genuine conditional support/overlap failure (Melly-Santangelo Assumption 4). +_ENVELOPE_SHARE_WARN = 0.10 + # Minimum share of finite bootstrap replicate rows required to report SEs # (bootstrap_utils convention). _MIN_VALID_REPLICATE_SHARE = 0.5 @@ -98,22 +232,38 @@ # ============================================================================= -def _build_cells(y: np.ndarray, g: np.ndarray, t: np.ndarray) -> Optional[Dict[str, np.ndarray]]: +def _build_cells( + y: np.ndarray, g: np.ndarray, t: np.ndarray, X: Optional[np.ndarray] = None +) -> Optional[Dict[str, np.ndarray]]: """Split outcomes into the four sorted (group, period) cells. Returns ``None`` if any cell is empty (bootstrap replicates use this to signal a failed draw; ``fit`` raises instead via :func:`_split_cells`). + + With covariates, each ``y..`` cell gains a row-aligned ``x..`` design block + via a PAIRED stable argsort: the sorted outcome values are identical to the + no-covariate ``np.sort`` (so every sorted-y invariant is preserved), while + the ``(y_i, x_i)`` pairing the quantile-regression path depends on stays + intact. ``x11`` is stored for symmetry but unused by both estimators. """ cells = {} for key, (gv, tv) in {"y00": (0, 0), "y01": (0, 1), "y10": (1, 0), "y11": (1, 1)}.items(): - cell = y[(g == gv) & (t == tv)] + mask = (g == gv) & (t == tv) + cell = y[mask] if cell.size == 0: return None - cells[key] = np.sort(cell) + if X is None: + cells[key] = np.sort(cell) + else: + order = np.argsort(cell, kind="stable") + cells[key] = cell[order] + cells["x" + key[1:]] = X[mask][order] return cells -def _split_cells(y: np.ndarray, g: np.ndarray, t: np.ndarray) -> Dict[str, np.ndarray]: +def _split_cells( + y: np.ndarray, g: np.ndarray, t: np.ndarray, X: Optional[np.ndarray] = None +) -> Dict[str, np.ndarray]: """Like :func:`_build_cells` but raises on empty cells (Assumption 5.1(ii)).""" for key, (gv, tv) in {"y00": (0, 0), "y01": (0, 1), "y10": (1, 0), "y11": (1, 1)}.items(): if not np.any((g == gv) & (t == tv)): @@ -121,7 +271,7 @@ def _split_cells(y: np.ndarray, g: np.ndarray, t: np.ndarray) -> Dict[str, np.nd f"Empty (group, period) cell: no observations in the {_CELL_LABELS[key]} cell. " "All four 2x2 cells must be non-empty (Athey-Imbens Assumption 5.1(ii))." ) - cells = _build_cells(y, g, t) + cells = _build_cells(y, g, t, X) assert cells is not None return cells @@ -163,6 +313,78 @@ def _quantile_type7(sorted_sample: np.ndarray, probs: np.ndarray) -> np.ndarray: return np.quantile(sorted_sample, p, method="linear") +def _rq_fit(y: np.ndarray, X: np.ndarray, taus: np.ndarray) -> Optional[np.ndarray]: + """Linear quantile regression via the Koenker-Bassett LP, one solve per tau. + + Matches R ``quantreg::rq(y ~ X, tau=taus)`` (default Barrodale-Roberts + simplex): both solvers return an exact-vertex solution, and with a + continuous outcome the optimum is generically unique, so coefficients + agree to ~1e-13. Primal formulation: variables ``[beta (free), u+ >= 0, + u- >= 0]`` with ``X_design @ beta + u+ - u- = y`` and objective + ``tau * sum(u+) + (1 - tau) * sum(u-)``, solved by HiGHS. + + Returns the ``(len(taus), k+1)`` coefficient matrix (intercept first, + matching R's ``(Intercept)`` row), or ``None`` if any tau's LP fails - + bootstrap replicates turn that into a NaN row; ``fit`` raises. + """ + n = y.shape[0] + X_design = np.column_stack([np.ones(n), X]) + p = X_design.shape[1] + A_eq = np.hstack([X_design, np.eye(n), -np.eye(n)]) + bounds = [(None, None)] * p + [(0.0, None)] * (2 * n) + coefs = np.empty((taus.shape[0], p)) + for j, tau in enumerate(taus): + c = np.concatenate([np.zeros(p), np.full(n, tau), np.full(n, 1.0 - tau)]) + res = linprog(c, A_eq=A_eq, b_eq=y, bounds=bounds, method="highs") + if res.status != 0 or res.x is None or not np.all(np.isfinite(res.x[:p])): + return None + coefs[j] = res.x[:p] + return coefs + + +def _design_matrix(x_cell: np.ndarray) -> np.ndarray: + """Prepend the intercept column to a cell's covariate block.""" + return np.column_stack([np.ones(x_cell.shape[0]), x_cell]) + + +def _fhat_eval(preds: np.ndarray, taus: np.ndarray, y: np.ndarray) -> np.ndarray: + """quantreg ``predict.rqs(type="Fhat", stepfun=TRUE)`` evaluated at ``y``. + + Verbatim port of the R construction (per observation): sort the 99 + predicted quantiles (``o = order(pred)``, stable), build + ``stepfun(pred[o], taus_ext[c(1, o)])`` with ``taus_ext = c(taus[1], + taus)``, and evaluate right-continuously. The floor is ``taus[0]`` (never + 0), and the convention carries a deliberate one-step lag relative to the + "natural" CDF assignment - do not "fix" it; ranks must land exactly on the + values qte produces because the downstream Qhat lookup is knot-exact. + """ + n_obs = y.shape[0] + taus_ext = np.concatenate([taus[:1], taus]) + ranks = np.empty(n_obs) + for i in range(n_obs): + o = np.argsort(preds[i], kind="stable") + yvals = np.concatenate([taus_ext[:1], taus_ext[o]]) + ranks[i] = yvals[np.searchsorted(preds[i][o], y[i], side="right")] + return ranks + + +def _qhat_eval(preds: np.ndarray, taus: np.ndarray, ranks: np.ndarray) -> np.ndarray: + """quantreg ``predict.rqs(type="Qhat", stepfun=TRUE)`` evaluated at ``ranks``. + + Verbatim port: ``stepfun(taus, c(pred[1], pred))`` per observation - NO + sorting of the predicted quantiles (unlike Fhat) - evaluated + right-continuously. The ranks land exactly ON the tau knots by + construction, so the ``side="right"`` exact-knot semantics is the + parity-critical detail (bit-exact against R in the smoke spike). + """ + n_obs = ranks.shape[0] + out = np.empty(n_obs) + for i in range(n_obs): + yvals = np.concatenate([preds[i, :1], preds[i]]) + out[i] = yvals[np.searchsorted(taus, ranks[i], side="right")] + return out + + def _cic_point( cells: Dict[str, np.ndarray], quantiles: np.ndarray ) -> Tuple[float, np.ndarray, np.ndarray]: @@ -207,6 +429,76 @@ def _qdid_point(cells: Dict[str, np.ndarray], quantiles: np.ndarray) -> Tuple[fl return att, q1 - q0 +def _cic_point_cov( + cells: Dict[str, np.ndarray], quantiles: np.ndarray +) -> Optional[Tuple[float, np.ndarray, np.ndarray, np.ndarray]]: + """CiC with covariates - exact port of qte::CiC()'s ``xformla`` branch. + + Per-cell linear quantile regressions on the fixed 99-tau grid in the + control cells; each treated pre-period observation gets its conditional + rank ``Fhat_{00|X_i}(Y_i)`` and imputed counterfactual + ``y0t_i = Qhat_{01|X_i}(rank_i)``; the counterfactual distribution is the + empirical distribution of the imputations (integration over the treated + PRE-period covariate distribution - qte's convention; Melly-Santangelo + integrate over treated-post, see the REGISTRY Note). ATT and QTEs then + follow the unconditional arithmetic with type-1 quantiles on both sides. + + Returns ``(att, qte, y0t_sorted, envelope_flags)`` - the flags mark + observations outside their conditional quantile envelope for the + fit-level support diagnostic (ignored by the bootstrap) - or ``None`` + when any quantile-regression LP fails. + """ + coefs00 = _rq_fit(cells["y00"], cells["x00"], _QR_TAU_GRID) + coefs01 = _rq_fit(cells["y01"], cells["x01"], _QR_TAU_GRID) + if coefs00 is None or coefs01 is None: + return None + x10_design = _design_matrix(cells["x10"]) + preds00 = x10_design @ coefs00.T + preds01 = x10_design @ coefs01.T + ranks = _fhat_eval(preds00, _QR_TAU_GRID, cells["y10"]) + y0t = _qhat_eval(preds01, _QR_TAU_GRID, ranks) + att = float(np.mean(cells["y11"]) - np.mean(y0t)) + y0t_sorted = np.sort(y0t) + qte = _quantile_type1(cells["y11"], quantiles) - _quantile_type1(y0t_sorted, quantiles) + envelope_flags = (cells["y10"] < preds00.min(axis=1)) | (cells["y10"] >= preds00.max(axis=1)) + return att, qte, y0t_sorted, envelope_flags + + +def _qdid_point_cov( + cells: Dict[str, np.ndarray], quantiles: np.ndarray +) -> Optional[Tuple[float, np.ndarray]]: + """QDiD with covariates - exact port of qte::QDiD()'s ``xformla`` branch. + + Quantile regressions in THREE cells; the conditional rank comes from the + treated pre-period cell's OWN conditional distribution, and the imputation + is additive: ``y0t_i = Y_i + Qhat_{01|X_i}(rank_i) - Qhat_{00|X_i}(rank_i)``. + + Asymmetric quantile types, ported verbatim (qte wart, REGISTRY Note): + ``q1`` uses R's DEFAULT type-7 on the treated post-period sample while + ``q0`` is the type-1 quantile of the imputed sample (via ``quantile.ecdf`` + in R, which reconstructs the sample exactly). + """ + coefs00 = _rq_fit(cells["y00"], cells["x00"], _QR_TAU_GRID) + coefs01 = _rq_fit(cells["y01"], cells["x01"], _QR_TAU_GRID) + coefs10 = _rq_fit(cells["y10"], cells["x10"], _QR_TAU_GRID) + if coefs00 is None or coefs01 is None or coefs10 is None: + return None + x10_design = _design_matrix(cells["x10"]) + preds10 = x10_design @ coefs10.T + preds01 = x10_design @ coefs01.T + preds00 = x10_design @ coefs00.T + ranks = _fhat_eval(preds10, _QR_TAU_GRID, cells["y10"]) + y0t = ( + cells["y10"] + + _qhat_eval(preds01, _QR_TAU_GRID, ranks) + - _qhat_eval(preds00, _QR_TAU_GRID, ranks) + ) + att = float(np.mean(cells["y11"]) - np.mean(y0t)) + q1 = _quantile_type7(cells["y11"], quantiles) + q0 = _quantile_type1(np.sort(y0t), quantiles) + return att, q1 - q0 + + def _interior_range(cells: Dict[str, np.ndarray]) -> Tuple[float, float]: """Eq. (17) plug-in interior range for CiC quantile effects. @@ -220,17 +512,27 @@ def _interior_range(cells: Dict[str, np.ndarray]) -> Tuple[float, float]: return q_lower, q_upper -def _parse_2x2_formula(formula: str, data: pd.DataFrame) -> Tuple[str, str, str]: - """Parse ``"outcome ~ treatment * time"`` style 2x2 formulas. +def _parse_2x2_formula( + formula: str, data: pd.DataFrame +) -> Tuple[str, str, str, Optional[List[str]]]: + """Parse ``"outcome ~ treatment * time [+ covariates]"`` style 2x2 formulas. Mirrors the DifferenceInDifferences formula grammar for the interaction - forms; covariate terms are rejected (covariates are deferred from v1). + forms, with TRAILING covariate terms. Deliberate deviations from the DiD + parser: in the ``:`` form, BOTH interaction-pair members must appear as + main effects and roles come from the MAIN-EFFECT order (not the + interaction-term order) - CiC/QDiD are not symmetric in (treatment, time), + and ``treated:post`` vs ``post:treated`` must not silently swap semantics. + Leading covariates (``"y ~ x1 + treat * post"``) are unsupported and + surface as a column-not-found error on the malformed term - list + covariates after the interaction. """ if "~" not in formula: raise ValueError("Formula must contain '~' to separate outcome from predictors") lhs, rhs = formula.split("~", 1) outcome = lhs.strip() rhs = rhs.strip() + covariates: Optional[List[str]] = None if "*" in rhs: parts = [p.strip() for p in rhs.split("*")] @@ -238,10 +540,9 @@ def _parse_2x2_formula(formula: str, data: pd.DataFrame) -> Tuple[str, str, str] raise ValueError("Currently only supports single interaction (treatment * time)") treatment, time = parts if "+" in time: - raise ValueError( - "Covariates are not supported by ChangesInChanges/QDiD (deferred from v1; " - "see the Melly-Santangelo covariate route in the methodology registry)." - ) + time_parts = [p.strip() for p in time.split("+")] + time = time_parts[0] + covariates = time_parts[1:] elif ":" in rhs: terms = [t.strip() for t in rhs.split("+")] interaction = None @@ -260,24 +561,33 @@ def _parse_2x2_formula(formula: str, data: pd.DataFrame) -> Tuple[str, str, str] pair = [p.strip() for p in interaction.split(":")] if len(pair) != 2: raise ValueError("Interaction term must involve exactly two variables") - if sorted(mains) != sorted(pair): + if pair[0] == pair[1]: + raise ValueError("Interaction term must involve two distinct variables") + if pair[0] not in mains or pair[1] not in mains: raise ValueError( - "Covariates are not supported by ChangesInChanges/QDiD (deferred from v1; " - "the formula must be 'outcome ~ treatment + time + treatment:time')." + "Both variables in the interaction term must also appear as main effects " + "('outcome ~ treatment + time + treatment:time [+ covariates]'); roles are " + "taken from the main-effect order." ) - # Roles come from the MAIN-EFFECT order, not the interaction-term order: - # CiC/QDiD are not symmetric in (treatment, time), and 'treated:post' vs - # 'post:treated' must not silently swap semantics. - treatment, time = mains[0], mains[1] + # Roles come from the MAIN-EFFECT order, not the interaction-term order; + # remaining main effects are covariates. First occurrences only, so a + # duplicated main effect cannot corrupt the role assignment. + role_mains: List[str] = [] + for m in mains: + if m in pair and m not in role_mains: + role_mains.append(m) + treatment, time = role_mains[0], role_mains[1] + extras = [m for m in mains if m not in pair] + covariates = extras if extras else None else: raise ValueError( "Formula must include an interaction term (treatment * time or treatment:time)" ) - for name in (outcome, treatment, time): + for name in (outcome, treatment, time, *(covariates or [])): if name not in data.columns: raise ValueError(f"Column '{name}' from formula not found in data") - return outcome, treatment, time + return outcome, treatment, time, covariates # ============================================================================= @@ -299,10 +609,39 @@ def _check_support(cells: Dict[str, np.ndarray]) -> None: ) +def _check_conditional_support(envelope_flags: np.ndarray) -> None: + """Warn on conditional support failure under covariates (CiC). + + ``envelope_flags`` marks treated pre-period observations whose outcome + falls outside the conditional quantile envelope spanned by their 99 + predicted control pre-period quantiles - exactly the observations whose + conditional rank is the extrapolated floor/ceiling plateau of the Fhat + step function. The check covers the rank cell only (control pre-period QR + at the treated observation's covariates) - a documented design choice; see + the REGISTRY Note. ~2% outside is expected under correct specification + (the envelope spans taus 0.01-0.99), hence the 10% threshold. + """ + share = float(np.mean(envelope_flags)) + if share > _ENVELOPE_SHARE_WARN: + n_out = int(np.count_nonzero(envelope_flags)) + warnings.warn( + f"{n_out} of {envelope_flags.size} treated pre-period outcomes ({share:.0%}) fall " + "outside their conditional quantile envelope (the span of the 99 predicted " + "control pre-period grid quantiles, taus 0.01-0.99, at their own covariates). " + "This suggests the conditional support/overlap condition " + "(Melly-Santangelo 2015, Assumption 4 - the covariate analogue of Athey-Imbens " + "Assumption 3.4) fails: conditional ranks for these observations are extrapolated " + "tail plateaus, and the counterfactual involves out-of-support extrapolation.", + UserWarning, + stacklevel=2, + ) + + def _check_ties(cells: Dict[str, np.ndarray]) -> None: - """Warn on heavy ties (discrete-looking outcomes) in any cell.""" + """Warn on heavy ties (discrete-looking outcomes) in any outcome cell.""" max_share = 0.0 - for cell in cells.values(): + for key in ("y00", "y01", "y10", "y11"): + cell = cells[key] share = 1.0 - np.unique(cell).size / cell.size max_share = max(max_share, share) if max_share > _TIE_SHARE_WARN: @@ -343,7 +682,7 @@ def _check_qdid_monotonicity(cells: Dict[str, np.ndarray], quantiles: np.ndarray def _bootstrap_replicates( - point_fn: Callable[[Dict[str, np.ndarray], np.ndarray], Tuple[float, np.ndarray]], + point_fn: Callable[[Dict[str, np.ndarray], np.ndarray], Optional[Tuple[Any, ...]]], y: np.ndarray, g: np.ndarray, t: np.ndarray, @@ -352,14 +691,21 @@ def _bootstrap_replicates( n_bootstrap: int, quantiles: np.ndarray, rng: np.random.Generator, + X: Optional[np.ndarray] = None, ) -> np.ndarray: """Bootstrap replicate matrix, shape ``(n_bootstrap, 1 + K)`` (col 0 = ATT). Resampling matches qte 1.3.1: panel mode samples unit ids with replacement (each unit's two periods travel together); repeated cross-section mode draws one pooled row resample of the stacked two-period data (unstratified, - so cell sizes vary across draws). Replicates with an empty cell produce a - NaN row rather than an exception. + so cell sizes vary across draws). Covariates travel with the resampled + rows, and the covariate point functions refit every per-cell quantile + regression inside each replicate (qte's ``bootiter`` re-runs the whole + estimator). Replicates with an empty cell - or, on the covariate path, a + failed quantile-regression LP - produce a NaN row rather than an + exception. The RNG draw sequence is identical with and without covariates + (the quantile regressions consume no randomness), preserving seed + determinism. """ n_cols = 1 + quantiles.shape[0] out = np.full((n_bootstrap, n_cols), np.nan) @@ -369,11 +715,14 @@ def _bootstrap_replicates( # Pre-pivot to unit-level arrays: one (y_pre, y_post, group) triple per unit. order = np.argsort(unit_ids, kind="stable") uid, y_o, g_o, t_o = unit_ids[order], y[order], g[order], t[order] + X_o = X[order] if X is not None else None pre_mask = t_o == 0 # Balanced panel (enforced in fit): each unit has exactly one pre and one post row. y_pre = y_o[pre_mask] y_post = y_o[~pre_mask] g_unit = g_o[pre_mask] + X_pre = X_o[pre_mask] if X_o is not None else None + X_post = X_o[~pre_mask] if X_o is not None else None # uid is sorted, so pre/post slices align unit-by-unit. assert np.array_equal(uid[pre_mask], uid[~pre_mask]) n_units = y_pre.shape[0] @@ -382,22 +731,33 @@ def _bootstrap_replicates( gb = g_unit[idx] yb = np.concatenate([y_pre[idx], y_post[idx]]) tb = np.concatenate([np.zeros(n_units), np.ones(n_units)]) - cells = _build_cells(yb, np.concatenate([gb, gb]), tb) + # Stacking order matches yb: pre-period rows first, then post. + Xb = ( + np.vstack([X_pre[idx], X_post[idx]]) + if X_pre is not None and X_post is not None + else None + ) + cells = _build_cells(yb, np.concatenate([gb, gb]), tb, Xb) if cells is None: continue - att_b, qte_b = point_fn(cells, quantiles)[:2] - out[b, 0] = att_b - out[b, 1:] = qte_b + res_b = point_fn(cells, quantiles) + if res_b is None: + continue + out[b, 0] = res_b[0] + out[b, 1:] = res_b[1] else: n_rows = y.shape[0] for b in range(n_bootstrap): idx = rng.integers(0, n_rows, n_rows) - cells = _build_cells(y[idx], g[idx], t[idx]) + Xb = X[idx] if X is not None else None + cells = _build_cells(y[idx], g[idx], t[idx], Xb) if cells is None: continue - att_b, qte_b = point_fn(cells, quantiles)[:2] - out[b, 0] = att_b - out[b, 1:] = qte_b + res_b = point_fn(cells, quantiles) + if res_b is None: + continue + out[b, 0] = res_b[0] + out[b, 1:] = res_b[1] return out @@ -455,6 +815,7 @@ def _fit_distributional( treatment: Optional[str], time: Optional[str], formula: Optional[str], + covariates: Optional[List[str]], unit: Optional[str], kind: str, ) -> ChangesInChangesResults: @@ -464,15 +825,61 @@ def _fit_distributional( quantiles = np.sort( np.asarray(_DEFAULT_QUANTILES if est.quantiles is None else est.quantiles, dtype=float) ) + estimator_name = "ChangesInChanges" if kind == "cic" else "QDiD" # ---- column resolution ------------------------------------------------- if formula is not None: - outcome, treatment, time = _parse_2x2_formula(formula, data) + # Uniform strictness (deliberately stricter than DifferenceInDifferences, + # which silently lets the formula win over explicit kwargs): mixing + # formula with explicit column arguments is ambiguous - reject it. + supplied = [ + name + for name, value in ( + ("outcome", outcome), + ("treatment", treatment), + ("time", time), + ("covariates", covariates), + ) + if value is not None + ] + if supplied: + raise ValueError( + "Provide either 'formula' or explicit column arguments, not both " + f"(got formula= together with {supplied})." + ) + outcome, treatment, time, covariates = _parse_2x2_formula(formula, data) elif outcome is None or treatment is None or time is None: raise ValueError( "Must provide either 'formula' or all of 'outcome', 'treatment', and 'time'" ) - used_cols = [outcome, treatment, time] + + # ---- covariate validation ------------------------------------------------ + if isinstance(covariates, (str, bytes)): + # A bare string would iterate character-wise ("x1" -> ["x", "1"]) and + # could silently fit the wrong covariate set if such columns exist. + raise ValueError( + f"covariates must be a list of column names, got the bare string " + f"{covariates!r} - did you mean covariates=[{covariates!r}]?" + ) + if covariates is not None and len(covariates) == 0: + covariates = None + if covariates is not None: + covariates = [str(c) for c in covariates] + reserved = {outcome, treatment, time} + if est.panel and unit is not None: + reserved.add(unit) + validate_covariate_names(covariates, reserved, estimator=estimator_name) + for col in covariates: + if col not in data.columns: + raise ValueError(f"Covariate column '{col}' not found in data") + if not pd.api.types.is_numeric_dtype(data[col]): + raise ValueError( + f"Covariate column '{col}' is not numeric. {estimator_name} accepts " + "numeric covariates only - dummy-encode categorical variables first " + "(e.g. pd.get_dummies(data, columns=[...]))." + ) + + used_cols = [outcome, treatment, time] + (covariates or []) if est.panel: if unit is None: raise ValueError("'unit' is required when panel=True (unit identifier column)") @@ -503,6 +910,15 @@ def _fit_distributional( "(inf/-inf). Clean or drop these observations before fitting - they would " "silently corrupt the empirical CDFs, quantiles, and bootstrap." ) + if covariates is not None: + x_check = frame[covariates].to_numpy(dtype=float) + if not np.all(np.isfinite(x_check)): + n_nonfinite = int(np.count_nonzero(~np.isfinite(x_check))) + raise ValueError( + f"Covariate column(s) {covariates} contain {n_nonfinite} non-finite " + "value(s) (inf/-inf). Clean or drop these observations before fitting - " + "they would silently corrupt the quantile regressions and bootstrap." + ) validate_binary(frame[treatment].to_numpy(dtype=float), "treatment") validate_binary(frame[time].to_numpy(dtype=float), "time") @@ -540,25 +956,71 @@ def _fit_distributional( y = frame[outcome].to_numpy(dtype=float) g = frame[treatment].to_numpy(dtype=float).astype(np.int64) t = frame[time].to_numpy(dtype=float).astype(np.int64) + # Extracted AFTER panel balancing so dropped-unit rows leave X consistently. + X = frame[covariates].to_numpy(dtype=float) if covariates is not None else None # ---- cells + diagnostics ----------------------------------------------- - cells = _split_cells(y, g, t) + cells = _split_cells(y, g, t, X) + if X is not None: + # Fail closed at fit: below k+2 rows the quantile-regression LP has + # exact-fit degeneracy (rq produces garbage or errors there too). + # Bootstrap replicates instead NaN-row on LP failure, matching qte, + # which has no size guard. + k = X.shape[1] + qr_cells = ("y00", "y01") if kind == "cic" else ("y00", "y01", "y10") + for key in qr_cells: + if cells[key].size < k + 2: + raise ValueError( + f"Too few observations for quantile regression in the " + f"{_CELL_LABELS[key]} cell: {cells[key].size} row(s) with {k} " + f"covariate(s) (need at least k + 2 = {k + 2})." + ) _check_ties(cells) if kind == "cic": - _check_support(cells) - q_lower, q_upper = _interior_range(cells) + if covariates is None: + _check_support(cells) + q_lower, q_upper = _interior_range(cells) + else: + # The eq. (17) interior range is an unconditional-distribution + # object; under covariates it is not the relevant bound (and qte + # applies no guard). The conditional-envelope diagnostic below is + # the covariate-path support check. + q_lower, q_upper = np.nan, np.nan else: - _check_qdid_monotonicity(cells, quantiles) + if covariates is None: + # Under covariates the check is moot: the covariate-path q0 is a + # type-1 quantile of the imputed sample, monotone by construction. + _check_qdid_monotonicity(cells, quantiles) q_lower, q_upper = np.nan, np.nan # ---- point estimation --------------------------------------------------- + qr_failure_msg = ( + "Quantile regression failed in at least one (group, period) cell (the LP " + "solver did not converge). This typically indicates degenerate or collinear " + "covariates; check the covariate design within each 2x2 cell." + ) if kind == "cic": - att, qte, _ = _cic_point(cells, quantiles) - point_fn: Callable[..., Any] = _cic_point + if covariates is None: + att, qte, _ = _cic_point(cells, quantiles) + point_fn: Callable[..., Any] = _cic_point + else: + cic_cov = _cic_point_cov(cells, quantiles) + if cic_cov is None: + raise ValueError(qr_failure_msg) + att, qte, _, envelope_flags = cic_cov + _check_conditional_support(envelope_flags) + point_fn = _cic_point_cov context = "ChangesInChanges bootstrap" else: - att, qte = _qdid_point(cells, quantiles) - point_fn = _qdid_point + if covariates is None: + att, qte = _qdid_point(cells, quantiles) + point_fn = _qdid_point + else: + qdid_cov = _qdid_point_cov(cells, quantiles) + if qdid_cov is None: + raise ValueError(qr_failure_msg) + att, qte = qdid_cov + point_fn = _qdid_point_cov context = "QDiD bootstrap" # ---- bootstrap ---------------------------------------------------------- @@ -566,7 +1028,7 @@ def _fit_distributional( if est.n_bootstrap > 0: rng = np.random.default_rng(est.seed) replicates = _bootstrap_replicates( - point_fn, y, g, t, unit_ids, est.panel, est.n_bootstrap, quantiles, rng + point_fn, y, g, t, unit_ids, est.panel, est.n_bootstrap, quantiles, rng, X=X ) # sup_t_crit is computed over ALL grid columns BEFORE the interior-range # NaN overwrite below (qte has no interior guard; excluding guarded @@ -589,7 +1051,11 @@ def _fit_distributional( t_stat, p_value, conf_int = safe_inference(att, att_se, est.alpha) t_stats, p_values, ci_lo, ci_hi = safe_inference_batch(qte, qte_ses, est.alpha) - if kind == "cic": + # The covariates-is-None guard is load-bearing: with covariates active, + # q_lower = q_upper = NaN and the exterior mask below would evaluate + # ALL-TRUE (NaN comparisons are False), silently NaN-ing every quantile's + # inference. qte applies no interior guard on the covariate path. + if kind == "cic" and covariates is None: exterior = ~((quantiles > q_lower) & (quantiles < q_upper)) if np.any(exterior): warnings.warn( @@ -643,6 +1109,7 @@ def _fit_distributional( estimator=kind, quantiles=quantiles, alpha=est.alpha, + covariates=list(covariates) if covariates is not None else None, ) est.results_ = results est.is_fitted_ = True @@ -736,7 +1203,21 @@ class ChangesInChanges: ----- Quantile effects are point-identified only on the eq. (17) interior range ``(q_lower, q_upper)``; effects outside it keep their point estimates (qte - parity) but report NaN inference with a warning. + parity) but report NaN inference with a warning. This guard applies to + unconditional (no-covariate) fits only: with covariates the eq. (17) + bounds are not the relevant objects (``q_lower``/``q_upper`` are NaN) and + a conditional support diagnostic replaces the unconditional one. + + Covariates (``covariates=`` at fit time, or trailing formula terms) port + qte's ``xformla`` branch exactly: linear quantile regressions of the + outcome on the covariates within the control pre- and post-period cells on + qte's fixed internal 0.01-0.99 tau grid (99 points, not user-configurable), + conditional-rank imputation per treated pre-period observation, and the + same bootstrap schemes with every quantile regression refit inside each + replicate. Covariates must be numeric (dummy-encode categoricals). Runtime + note: a covariate fit solves roughly ``2 x 99 x (1 + n_bootstrap)`` small + linear programs (~40k at the default ``n_bootstrap=200``) - typically tens + of seconds at moderate cell sizes, the same cost profile as ``qte::CiC``. Additive random group-time shocks (random effects at the group x period level) BIAS the CiC estimator - unlike linear DiD, where they only @@ -744,8 +1225,10 @@ class ChangesInChanges: Imbens 2006, p. 476). With more than two groups/periods they are testable (Theorem 6.4), but that extension is deferred. - Covariates, discrete-outcome bounds, and analytical standard errors are - deferred from v1 and documented in docs/methodology/REGISTRY.md. + The full Melly-Santangelo (2015) covariate estimator (monotonized + integrated-indicator CDFs, treated-post covariate integration, + exchangeable bootstrap), discrete-outcome bounds, and analytical standard + errors remain deferred and documented in docs/methodology/REGISTRY.md. """ def __init__( @@ -808,6 +1291,7 @@ def fit( treatment: Optional[str] = None, time: Optional[str] = None, formula: Optional[str] = None, + covariates: Optional[List[str]] = None, unit: Optional[str] = None, ) -> ChangesInChangesResults: """Fit the CiC estimator on a 2x2 dataset. @@ -821,13 +1305,25 @@ def fit( treated group in BOTH periods), binary post-period indicator. Required unless ``formula`` is given. formula : str, optional - R-style 2x2 formula, e.g. ``"y ~ treated * post"``. Covariate - terms raise (deferred from v1). + R-style 2x2 formula, e.g. ``"y ~ treated * post"`` or + ``"y ~ treated * post + x1 + x2"`` (trailing terms are + covariates). Mixing ``formula`` with any explicit column argument + raises - deliberately stricter than ``DifferenceInDifferences``, + which silently lets the formula win. + covariates : list of str, optional + Numeric covariate columns for the conditional (quantile- + regression) CiC - qte's ``xformla``. Fit-time argument only (not a + hyperparameter; absent from ``get_params()``, like ``unit``). + Dummy-encode categorical covariates first. In panel mode, + covariates may be time-varying: each (group, period) cell uses its + own rows' covariate values, exactly like qte. unit : str, optional Unit identifier column. Required when ``panel=True``; ignored (documented) when ``panel=False``, matching qte's ``idname``. """ - return _fit_distributional(self, data, outcome, treatment, time, formula, unit, "cic") + return _fit_distributional( + self, data, outcome, treatment, time, formula, covariates, unit, "cic" + ) class QDiD: @@ -845,11 +1341,20 @@ class QDiD: p. 447): QDiD's justifying model is not invariant to monotone transformations of the outcome, forces the unobservable distribution to be identical across all four cells, and places testable restrictions on the - data (a warning fires when the implied counterfactual quantile function is - non-monotone). QDiD's mean effect matches standard DiD's ATT in + data (in unconditional fits, a warning fires when the implied + counterfactual quantile function is non-monotone; with covariates the + check is moot - the imputed counterfactual's quantile curve is monotone by + construction). QDiD's mean effect matches standard DiD's ATT in population; the paper provides no asymptotic theory for QDiD, so inference is a bootstrap convention shared with the qte package. + Covariates port qte's ``xformla`` branch exactly: quantile regressions in + THREE cells (both control cells plus treated-pre), conditional ranks from + the treated pre-period cell's own conditional distribution, and an + additive imputation. qte's covariate QDiD mixes quantile types - type-7 + for the treated post-period quantiles, type-1 for the imputed + counterfactual - and that asymmetry is ported verbatim (REGISTRY Note). + Constructor parameters, fit signature, bootstrap behavior, and the results container are identical to :class:`ChangesInChanges` (no interior-range guard: eq. 17 has no QDiD analogue). @@ -915,7 +1420,10 @@ def fit( treatment: Optional[str] = None, time: Optional[str] = None, formula: Optional[str] = None, + covariates: Optional[List[str]] = None, unit: Optional[str] = None, ) -> ChangesInChangesResults: """Fit the QDiD estimator on a 2x2 dataset (see ChangesInChanges.fit).""" - return _fit_distributional(self, data, outcome, treatment, time, formula, unit, "qdid") + return _fit_distributional( + self, data, outcome, treatment, time, formula, covariates, unit, "qdid" + ) diff --git a/diff_diff/changes_in_changes_results.py b/diff_diff/changes_in_changes_results.py index 1b48ac29..9baae82a 100644 --- a/diff_diff/changes_in_changes_results.py +++ b/diff_diff/changes_in_changes_results.py @@ -1,7 +1,7 @@ """Results container for the ChangesInChanges (CiC) and QDiD estimators.""" from dataclasses import dataclass, field -from typing import Any, Dict, Tuple +from typing import Any, Dict, List, Optional, Tuple import numpy as np import pandas as pd @@ -26,9 +26,13 @@ class ChangesInChangesResults: is NaN. ``q_lower``/``q_upper`` bound the point-identified interior quantile range - for CiC (Athey-Imbens eq. 17; NaN for QDiD). ``sup_t_crit`` is the qte - package's sup-t critical value for uniform bands - computed at a FIXED 95% - level regardless of ``alpha`` (qte parity); see :meth:`uniform_bands`. + for unconditional CiC fits (Athey-Imbens eq. 17; NaN for QDiD and for + covariate fits, where the unconditional bounds are not the relevant + objects). ``sup_t_crit`` is the qte package's sup-t critical value for + uniform bands - computed at a FIXED 95% level regardless of ``alpha`` (qte + parity); see :meth:`uniform_bands`. ``covariates`` records the covariate + columns used by the conditional (quantile-regression) fit, or ``None`` + for unconditional fits. """ att: float @@ -48,6 +52,7 @@ class ChangesInChangesResults: estimator: str quantiles: np.ndarray = field(repr=False) alpha: float = 0.05 + covariates: Optional[List[str]] = None # ------------------------------------------------------------------ # convenience properties @@ -73,8 +78,8 @@ def uniform_bands(self) -> pd.DataFrame: ``qte +/- sup_t_crit * se`` per quantile, using the qte package's IQR-scaled sup-t critical value at its hard-coded 95% level - the band level does NOT follow ``alpha`` (qte parity). Rows whose ``se`` is NaN - (no bootstrap, failed replicate gate, or outside the CiC interior - range) get NaN bands. + (no bootstrap, failed replicate gate, or outside the interior range in + an unconditional CiC fit) get NaN bands. """ qe = self.quantile_effects bands = pd.DataFrame( @@ -109,6 +114,7 @@ def to_dict(self) -> Dict[str, Any]: "panel": self.panel, "estimator": self.estimator, "alpha": self.alpha, + "covariates": list(self.covariates) if self.covariates else None, "inference_method": "bootstrap" if self.n_bootstrap > 0 else "none", } @@ -172,6 +178,11 @@ def _fmt(x: Any, nd: int = 4) -> str: f"treated post={cs.get('treated_post')}" ), ] + if self.covariates: + lines.append( + f"Covariates: {', '.join(self.covariates)} (conditional ranks via " + "per-cell linear quantile regression, 99-tau grid; qte xformla parity)" + ) if self.n_bootstrap > 0: lines.append( f"Inference: bootstrap ({self.n_bootstrap_valid}/{self.n_bootstrap} " diff --git a/diff_diff/guides/llms-full.txt b/diff_diff/guides/llms-full.txt index 37ac917b..0afd24fb 100644 --- a/diff_diff/guides/llms-full.txt +++ b/diff_diff/guides/llms-full.txt @@ -1030,7 +1030,7 @@ print(results.pooled) # pooled pre (placebo) / post (ATT) rows ### ChangesInChanges -Changes-in-Changes (Athey & Imbens 2006) for the canonical 2x2 design with continuous outcomes. Recovers the treated group's full counterfactual outcome distribution `F_10(F_00^{-1}(F_01(y)))` and reports the ATT plus quantile treatment effects on a grid (default 0.05-0.95 by 0.05, matching R `qte`). The model is invariant to monotone transformations of the outcome and, with continuous data, places no testable restrictions. Point estimation matches `qte::CiC()` (v1.3.1) exactly: R type-1 (ceiling-order-statistic) quantiles - the paper's empirical-inverse convention - throughout, no smoothing anywhere. Inference is bootstrap-only in this release: panel mode resamples units (both periods together), repeated cross-section mode draws a pooled row resample; SEs are replicate SDs with symmetric normal-approximation CIs, plus a sup-t critical value for uniform bands at a FIXED 95% level (qte parity - does not follow `alpha`). Quantile effects outside the point-identified interior range keep their point estimates but report NaN inference with a warning. Deferred (documented in REGISTRY.md): covariates, discrete-outcome bounds (a ties warning fires on discrete-looking outcomes), analytical SEs, staggered designs, treatment-on-controls. Note: additive random group-time shocks BIAS CiC (not just its inference) and are undetectable in a 2x2 design (Athey-Imbens p. 476). +Changes-in-Changes (Athey & Imbens 2006) for the canonical 2x2 design with continuous outcomes. Recovers the treated group's full counterfactual outcome distribution `F_10(F_00^{-1}(F_01(y)))` and reports the ATT plus quantile treatment effects on a grid (default 0.05-0.95 by 0.05, matching R `qte`). The model is invariant to monotone transformations of the outcome (exact for unconditional fits; the covariate QR branch is not equivariant to nonlinear monotone transforms) and, with continuous data, places no testable restrictions. Point estimation matches `qte::CiC()` (v1.3.1) exactly: R type-1 (ceiling-order-statistic) quantiles - the paper's empirical-inverse convention - throughout, no smoothing anywhere. Inference is bootstrap-only in this release: panel mode resamples units (both periods together), repeated cross-section mode draws a pooled row resample; SEs are replicate SDs with symmetric normal-approximation CIs, plus a sup-t critical value for uniform bands at a FIXED 95% level (qte parity - does not follow `alpha`). Quantile effects outside the point-identified interior range keep their point estimates but report NaN inference with a warning (unconditional fits; with covariates the interior bounds do not apply and `q_lower`/`q_upper` are NaN). Covariates (`covariates=[...]` or trailing formula terms) port qte's `xformla` branch exactly: linear quantile regressions in the control cells on qte's fixed internal 0.01-0.99 tau grid, conditional-rank imputation per treated pre-period observation, a conditional-envelope support warning (Melly-Santangelo Assumption 4), and quantile regressions refit inside every bootstrap replicate (~40k small LPs at n_bootstrap=200 - tens of seconds, same cost profile as qte). Covariates must be numeric (dummy-encode categoricals). Deferred (documented in REGISTRY.md): the full Melly-Santangelo covariate estimator, discrete-outcome bounds (a ties warning fires on discrete-looking outcomes), analytical SEs, staggered designs, treatment-on-controls. Note: additive random group-time shocks BIAS CiC (not just its inference) and are undetectable in a 2x2 design (Athey-Imbens p. 476). ```python ChangesInChanges( @@ -1050,7 +1050,8 @@ cic.fit( outcome: str = None, # Continuous outcome column treatment: str = None, # Binary group indicator (1 = treated group in BOTH periods) time: str = None, # Binary post-period indicator - formula: str = None, # Alternative: "y ~ treated * post" (covariate terms raise) + formula: str = None, # Alternative: "y ~ treated * post [+ x1 + x2]" (trailing terms = covariates; formula + explicit kwargs raises) + covariates: list[str] = None, # Numeric covariate columns (qte xformla parity; fit-time arg, not in get_params) unit: str = None, # Unit id; required when panel=True, ignored (documented) otherwise ) -> ChangesInChangesResults ``` @@ -1071,7 +1072,7 @@ print(results.uniform_bands()) # sup-t simultaneous bands (fixed 95%) ### QDiD -Quantile Difference-in-Differences comparison estimator (Athey & Imbens 2006, Section 3.3) for the 2x2 design: `QTE(tau) = Q(y11,tau) - [Q(y10,tau) + Q(y01,tau) - Q(y00,tau)]` with R type-7 linear-interpolation quantiles, matching `qte::QDiD()` (v1.3.1) exactly - including its ATT formula (control-group quantile functions evaluated at treated pre-period own-sample ranks; population-equivalent to the paper's k^QDID transformation but a different finite-sample estimator, see the REGISTRY.md Note). The paper recommends ChangesInChanges over QDiD: QDiD's justifying model is not scale-invariant, forces identical unobservable distributions in all four cells, and places testable restrictions on the data (a warning fires when the implied counterfactual quantile function is non-monotone). QDiD's mean effect equals standard DiD's ATT in population. Constructor, fit signature, bootstrap machinery, and results container are identical to ChangesInChanges (no interior-range guard; `q_lower`/`q_upper` are NaN). +Quantile Difference-in-Differences comparison estimator (Athey & Imbens 2006, Section 3.3) for the 2x2 design: `QTE(tau) = Q(y11,tau) - [Q(y10,tau) + Q(y01,tau) - Q(y00,tau)]` with R type-7 linear-interpolation quantiles, matching `qte::QDiD()` (v1.3.1) exactly - including its ATT formula (control-group quantile functions evaluated at treated pre-period own-sample ranks; population-equivalent to the paper's k^QDID transformation but a different finite-sample estimator, see the REGISTRY.md Note). The paper recommends ChangesInChanges over QDiD: QDiD's justifying model is not scale-invariant, forces identical unobservable distributions in all four cells, and places testable restrictions on the data (a warning fires when the implied counterfactual quantile function is non-monotone; unconditional fits only - the covariate-path counterfactual quantile curve is monotone by construction). QDiD's mean effect equals standard DiD's ATT in population. Constructor, fit signature (including `covariates=`), bootstrap machinery, and results container are identical to ChangesInChanges (no interior-range guard; `q_lower`/`q_upper` are NaN). Covariate fits use quantile regressions in THREE cells with own-cell conditional ranks and qte's verbatim-ported asymmetric quantile types (type-7 treated-post, type-1 imputed counterfactual). ```python QDiD( @@ -1723,7 +1724,7 @@ Per-horizon event-study results container for `HeterogeneousAdoptionDiD` with `a ### ChangesInChangesResults -Results container shared by `ChangesInChanges` and `QDiD` (the `estimator` field is `"cic"` or `"qdid"`; `QDiDResults` is an alias of this class). Flat-native headline fields `att`, `se`, `t_stat`, `p_value`, `conf_int`; `quantile_effects` is a DataFrame with columns `quantile`, `qte`, `se`, `t_stat`, `p_value`, `conf_low`, `conf_high`. `q_lower`/`q_upper` bound the CiC point-identified interior quantile range (NaN for QDiD); `sup_t_crit` is the qte sup-t critical value backing `uniform_bands()` (fixed 95% level). Also carries `n_obs`, `cell_sizes`, `n_bootstrap`, `n_bootstrap_valid`, `panel`, `quantiles`, `alpha`. Methods: `summary()`, `print_summary()`, `to_dict()`, `to_dataframe(level="quantiles"|"att")`, `uniform_bands()`. All inference flows through `safe_inference`/`safe_inference_batch` (joint-NaN contract; `n_bootstrap=0` yields NaN inference everywhere). +Results container shared by `ChangesInChanges` and `QDiD` (the `estimator` field is `"cic"` or `"qdid"`; `QDiDResults` is an alias of this class). Flat-native headline fields `att`, `se`, `t_stat`, `p_value`, `conf_int`; `quantile_effects` is a DataFrame with columns `quantile`, `qte`, `se`, `t_stat`, `p_value`, `conf_low`, `conf_high`. `q_lower`/`q_upper` bound the point-identified interior quantile range for unconditional CiC fits (NaN for QDiD and for covariate fits); `sup_t_crit` is the qte sup-t critical value backing `uniform_bands()` (fixed 95% level). Also carries `n_obs`, `cell_sizes`, `n_bootstrap`, `n_bootstrap_valid`, `panel`, `quantiles`, `alpha`, and `covariates` (the covariate columns of a conditional fit, else None). Methods: `summary()`, `print_summary()`, `to_dict()`, `to_dataframe(level="quantiles"|"att")`, `uniform_bands()`. All inference flows through `safe_inference`/`safe_inference_batch` (joint-NaN contract; `n_bootstrap=0` yields NaN inference everywhere). ### TROPResults diff --git a/diff_diff/guides/llms.txt b/diff_diff/guides/llms.txt index 2badbb9f..4027c4c3 100644 --- a/diff_diff/guides/llms.txt +++ b/diff_diff/guides/llms.txt @@ -71,8 +71,8 @@ Full practitioner guide: call `diff_diff.get_llm_guide("practitioner")` - [StaggeredTripleDifference](https://diff-diff.readthedocs.io/en/stable/api/staggered.html#staggeredtripledifference): Ortiz-Villavicencio & Sant'Anna (2025) staggered DDD with group-time ATT - [WooldridgeDiD](https://diff-diff.readthedocs.io/en/stable/api/wooldridge_etwfe.html): Wooldridge (2023, 2025) ETWFE — saturated OLS, logit/Poisson QMLE (ASF-based ATT). Alias: ETWFE - [LPDiD](https://diff-diff.readthedocs.io/en/stable/api/lpdid.html): Dube, Girardi, Jorda & Taylor (2025) Local Projections DiD: per-horizon long-difference event study on clean controls (no negative weighting); variance- or equally-weighted ATT, premean differencing, pooled pre/post, fast. Absorbing by default; non-absorbing (reversible) treatment via `non_absorbing="first_entry"` (Eq. 12) or `"effect_stabilization"` (Eq. 13, window `L`). Complex-survey designs (pweight + stratified-PSU TSL SEs) on the default path via `fit(survey_design=...)`. -- [ChangesInChanges](https://diff-diff.readthedocs.io/en/stable/api/changes_in_changes.html): Athey & Imbens (2006) nonlinear/distributional DiD for the 2x2 design: recovers the treated group's full counterfactual outcome distribution and quantile treatment effects (ATT + QTE grid) via the CDF transformation `F_10(F_00^{-1}(F_01(y)))`; invariant to monotone outcome transformations; bootstrap inference (panel or repeated cross-section resampling); point parity with R `qte::CiC()`. Continuous outcomes, no covariates in this release. Alias `CiC`. -- [QDiD](https://diff-diff.readthedocs.io/en/stable/api/changes_in_changes.html): Athey & Imbens (2006) quantile DiD comparison estimator (additive quantile-by-quantile DiD, matching R `qte::QDiD()`); same bootstrap machinery as ChangesInChanges. The paper recommends CiC over QDiD (scale-dependent model with testable restrictions; a non-monotonicity warning fires when violated). +- [ChangesInChanges](https://diff-diff.readthedocs.io/en/stable/api/changes_in_changes.html): Athey & Imbens (2006) nonlinear/distributional DiD for the 2x2 design: recovers the treated group's full counterfactual outcome distribution and quantile treatment effects (ATT + QTE grid) via the CDF transformation `F_10(F_00^{-1}(F_01(y)))`; invariant to monotone outcome transformations (unconditional fits; the covariate QR branch is not); bootstrap inference (panel or repeated cross-section resampling); point parity with R `qte::CiC()`, including its covariate branch (`covariates=` -> per-cell linear quantile regression, Melly-Santangelo-style conditional CiC). Continuous outcomes, numeric covariates. Alias `CiC`. +- [QDiD](https://diff-diff.readthedocs.io/en/stable/api/changes_in_changes.html): Athey & Imbens (2006) quantile DiD comparison estimator (additive quantile-by-quantile DiD, matching R `qte::QDiD()` including its covariate branch via `covariates=`); same bootstrap machinery as ChangesInChanges. The paper recommends CiC over QDiD (scale-dependent model with testable restrictions; a non-monotonicity warning fires when violated - unconditional fits only, the covariate-path counterfactual quantile curve is monotone by construction). - [BaconDecomposition](https://diff-diff.readthedocs.io/en/stable/api/bacon.html): Goodman-Bacon (2021) decomposition for diagnosing TWFE bias in staggered settings ## Diagnostics and Sensitivity Analysis diff --git a/docs/api/changes_in_changes.rst b/docs/api/changes_in_changes.rst index 8c5851fa..1d28c0b8 100644 --- a/docs/api/changes_in_changes.rst +++ b/docs/api/changes_in_changes.rst @@ -11,14 +11,16 @@ treatment effects on the treated alongside the ATT. ``F_10(F_00^{-1}(F_01(y)))``: each treated pre-period outcome is ranked in the control pre-period distribution and pushed through the control post-period quantile function. The model is invariant to monotone transformations of the -outcome (levels vs logs give consistent answers) and places no testable -restrictions on continuous data. +outcome (levels vs logs give consistent answers - a property the estimator +inherits exactly in unconditional fits; the covariate branch's linear +quantile regressions are not equivariant to nonlinear monotone transforms) +and places no testable restrictions on continuous data. ``QDiD`` is the quantile-by-quantile DiD comparison estimator the same paper formalizes: it adds the control group's over-time quantile change to the treated pre-period quantile. Athey & Imbens recommend CiC over QDiD - QDiD's -justifying model is not scale-invariant and imposes testable restrictions (a -warning fires when they appear violated). +justifying model is not scale-invariant and imposes testable restrictions (in +unconditional fits, a warning fires when they appear violated). .. note:: @@ -31,9 +33,20 @@ warning fires when they appear violated). both periods together, repeated cross-section mode draws a pooled row resample; SEs are replicate SDs with symmetric normal-approximation intervals, plus qte's sup-t critical value for uniform bands at a fixed - 95% level (independent of ``alpha``). Covariates, discrete-outcome bounds, - analytical standard errors, and staggered designs are deferred - see - ``docs/methodology/REGISTRY.md`` for the documented scope. + 95% level (independent of ``alpha``). + + Covariates are supported via qte's ``xformla``-parity route + (``covariates=[...]`` or trailing formula terms): per-cell linear quantile + regressions on qte's fixed internal 0.01-0.99 tau grid impute each treated + pre-period observation's conditional counterfactual (the Melly-Santangelo + 2015 pipeline in qte's simplified form). Covariates must be numeric + (dummy-encode categoricals), and every bootstrap replicate refits the + quantile regressions - a covariate fit at the default ``n_bootstrap=200`` + solves ~40k small linear programs (typically tens of seconds; the same cost + profile as ``qte::CiC``). Discrete-outcome bounds, analytical standard + errors, staggered designs, and the full Melly-Santangelo covariate + estimator remain deferred - see ``docs/methodology/REGISTRY.md`` for the + documented scope. **When to use ChangesInChanges:** @@ -41,9 +54,13 @@ warning fires when they appear violated). effect heterogeneity across the outcome distribution (which quantiles moved, not just the mean) - You want results invariant to monotone rescaling of the outcome + (unconditional fits; the covariate branch's linear quantile regressions are + not equivariant to nonlinear monotone transforms) - CiC quantile effects are point-identified on the interior range where the treated pre-period distribution overlaps the control pre-period support; effects outside it keep their point estimates but report NaN inference + (unconditional fits only - with covariates a conditional-envelope support + diagnostic applies instead and ``q_lower``/``q_upper`` are NaN) **Reference:** Athey, S., & Imbens, G. W. (2006). Identification and Inference in Nonlinear Difference-in-Differences Models. *Econometrica*, 74(2), 431-497. @@ -142,6 +159,23 @@ Estimate quantile treatment effects in a 2x2 design:: Panel mode (same units in both periods) changes only the bootstrap:: + import numpy as np + import pandas as pd + from diff_diff import ChangesInChanges + + rng = np.random.default_rng(0) + n = 400 + treated = np.repeat([1, 0], n // 2) + u = rng.normal(0, 1, n) + data = pd.DataFrame({ + "unit": np.tile(np.arange(n), 2), + "post": np.repeat([0, 1], n), + "treated": np.tile(treated, 2), + "y": np.concatenate( + [u + rng.normal(0, 0.3, n), u + 0.5 + rng.normal(0, 0.3, n) + treated] + ), + }) + cic_panel = ChangesInChanges(n_bootstrap=200, seed=42, panel=True) results_panel = cic_panel.fit( data, outcome="y", treatment="treated", time="post", unit="unit" @@ -154,6 +188,34 @@ QDiD as a comparison estimator:: qdid = QDiD(n_bootstrap=200, seed=42) results_qdid = qdid.fit(data, outcome="y", treatment="treated", time="post") +Covariates (conditional CiC via per-cell quantile regression, matching qte's +``xformla``; a small ``n_bootstrap`` keeps the example fast - every replicate +refits the quantile regressions):: + + import numpy as np + import pandas as pd + from diff_diff import ChangesInChanges + + rng = np.random.default_rng(1) + n = 120 + treated = np.repeat([1, 0], n // 2) + x1 = rng.uniform(0, 2, n) + 0.3 * treated + y_pre = 0.5 + 0.8 * x1 + rng.normal(0, 0.4, n) + y_post = 0.8 + 1.1 * x1 + rng.normal(0, 0.4, n) + treated * 0.7 + data = pd.DataFrame({ + "post": np.repeat([0, 1], n), + "treated": np.tile(treated, 2), + "x1": np.tile(x1, 2), + "y": np.concatenate([y_pre, y_post]), + }) + + cic_cov = ChangesInChanges(n_bootstrap=10, seed=42) + results_cov = cic_cov.fit( + data, outcome="y", treatment="treated", time="post", covariates=["x1"] + ) + # Equivalent formula route: "y ~ treated * post + x1" + print(results_cov.covariates) # ['x1'] + Comparison with related estimators ---------------------------------- diff --git a/docs/choosing_estimator.rst b/docs/choosing_estimator.rst index 6eaa06c4..7494c438 100644 --- a/docs/choosing_estimator.rst +++ b/docs/choosing_estimator.rst @@ -39,7 +39,7 @@ Start here and follow the questions: 4. **Do you have panel data?** (Multiple observations per unit over time) - **No** → Use :class:`~diff_diff.DifferenceInDifferences` (basic 2x2) - - **No, and you care about effect heterogeneity across the outcome distribution** → Use :class:`~diff_diff.ChangesInChanges` (2x2 quantile treatment effects, invariant to monotone outcome rescaling; works with panel data too - ``panel=True`` changes only the bootstrap). :class:`~diff_diff.QDiD` is the quantile-DiD comparison estimator; Athey & Imbens (2006) recommend CiC over it + - **No, and you care about effect heterogeneity across the outcome distribution** → Use :class:`~diff_diff.ChangesInChanges` (2x2 quantile treatment effects, invariant to monotone outcome rescaling in unconditional fits; optional numeric covariates via quantile-regression conditioning - the covariate branch's linear quantile regressions are not equivariant to nonlinear monotone transforms; works with panel data too - ``panel=True`` changes only the bootstrap). :class:`~diff_diff.QDiD` is the quantile-DiD comparison estimator; Athey & Imbens (2006) recommend CiC over it - **Yes** → Go to question 5 5. **Do you need period-specific effects?** (Event study design) diff --git a/docs/doc-deps.yaml b/docs/doc-deps.yaml index 10d2b5c7..b97f7dab 100644 --- a/docs/doc-deps.yaml +++ b/docs/doc-deps.yaml @@ -665,6 +665,9 @@ sources: type: methodology - path: docs/methodology/papers/athey-imbens-2006-review.md type: methodology + - path: docs/methodology/papers/melly-santangelo-2015-review.md + section: "covariate branch (qte xformla simplified form)" + type: methodology - path: docs/api/changes_in_changes.rst type: api_reference - path: README.md diff --git a/docs/methodology/REGISTRY.md b/docs/methodology/REGISTRY.md index 60b89848..1713e4a1 100644 --- a/docs/methodology/REGISTRY.md +++ b/docs/methodology/REGISTRY.md @@ -3768,7 +3768,7 @@ adjustment, deferred). **Primary source:** Athey, S., & Imbens, G. W. (2006). Identification and Inference in Nonlinear Difference-in-Differences Models. *Econometrica*, 74(2), 431-497. https://doi.org/10.1111/j.1468-0262.2006.00668.x -Full equation-level review (all equation/theorem/page pins below refer to the published version): `docs/methodology/papers/athey-imbens-2006-review.md`. Companion reviews for the deferred extensions: `callaway-li-oka-2018-review.md` (panel QTT / bootstrap validity machinery), `melly-santangelo-2015-review.md` (covariates), `ciaccio-2024-review.md` (staggered). +Full equation-level review (all equation/theorem/page pins below refer to the published version): `docs/methodology/papers/athey-imbens-2006-review.md`. Companion reviews: `melly-santangelo-2015-review.md` (covariates - the qte-style QR branch below is its simplified form; the full MS estimator remains deferred), `callaway-li-oka-2018-review.md` (panel QTT / bootstrap validity machinery, deferred), `ciaccio-2024-review.md` (staggered, deferred). **Model (Sections 2-3):** two groups x two periods; untreated outcomes generated by `Y^N = h(U, T)` with `h` strictly increasing in a scalar unobservable U (Assumptions 3.1-3.2), `U ⊥ T | G` (Assumption 3.3, time-invariance within groups), and support inclusion `U_1 ⊆ U_0` (Assumption 3.4, relaxable per Corollary 3.1). The counterfactual distribution of the treated group's untreated post-period outcome is `F_{Y^N,11}(y) = F_10(F_00^{-1}(F_01(y)))` (Theorem 3.1, eq. 9). With continuous outcomes the model imposes no testable restrictions and is invariant to monotone transformations of the outcome (p. 437-439). @@ -3776,7 +3776,7 @@ Full equation-level review (all equation/theorem/page pins below refer to the pu *Assumption checks / warnings:* - All four (group, period) cells non-empty (Assumption 5.1(ii)) -> `ValueError`. -- Support check: treated pre-period outcomes outside the control pre-period range (Assumption 3.4 / Corollary 3.1) -> `UserWarning`; quantile effects remain point-identified only on the eq. (17) interior range. +- Support check (unconditional fits): treated pre-period outcomes outside the control pre-period range (Assumption 3.4 / Corollary 3.1) -> `UserWarning`; quantile effects remain point-identified only on the eq. (17) interior range. Covariate fits replace this with the conditional-envelope diagnostic (see the covariates block below). - Heavy ties (> 10% duplicate values within a cell; library heuristic - the paper's Assumption 5.1(iii) is continuity, with no finite-sample ties rule, and this threshold is the library's operationalization of "discrete-looking" for the mixed continuous/discrete edge case) -> `UserWarning` citing the Section 4 bounds deferral: the continuous machinery applied to discrete data silently delivers one endpoint of the Athey-Imbens bounds, not a point estimate. - Docstring warning (not detectable in a 2x2 design): additive random group-time shocks BIAS CiC - unlike linear DiD where they only complicate inference; testable only with more than two groups/periods via Theorem 6.4 (p. 476; deferred). @@ -3789,7 +3789,16 @@ Full equation-level review (all equation/theorem/page pins below refer to the pu where `Q1` is the R type-1 quantile - the paper's eq. (35)/(A.1) inf-based ceiling-order-statistic inverse (`inf{y : F_hat(y) >= q}` = `Y_(ceil(Nq))`, `Q1(., 0)` = sample minimum) - and `ECDF` is the eq. (34) `<=`-semantics empirical CDF. The implementation ports R `quantile.default` type-1 arithmetic exactly, INCLUDING its `4 * .Machine$double.eps` fuzz on the index computation (`diff_diff/changes_in_changes.py::_quantile_type1`); this is where cross-language implementations diverge, and the type-1 micro-fixtures in `benchmarks/data/qte_golden.json` pin it bit-exactly against R. - **Note:** the estimator is deliberately smoothing-free (p. 451): empirical CDFs and order statistics only, no interpolating quantile definitions anywhere in the CiC pipeline. This yields exact monotone-transform equivariance (locked by `tests/test_methodology_changes_in_changes.py`). -*Interior-range guard (eq. 17 / Theorem 5.3):* `q_lower = ECDF_y10(min y00)`, `q_upper = ECDF_y10(max y00)`; requested quantiles outside the open interval `(q_lower, q_upper)` keep their point estimates (qte parity - `qte::CiC()` has no guard) but report NaN inference with a `UserWarning` (Theorem 5.3's asymptotics are interior-only). +*Interior-range guard (eq. 17 / Theorem 5.3; unconditional fits only):* `q_lower = ECDF_y10(min y00)`, `q_upper = ECDF_y10(max y00)`; requested quantiles outside the open interval `(q_lower, q_upper)` keep their point estimates (qte parity - `qte::CiC()` has no guard) but report NaN inference with a `UserWarning` (Theorem 5.3's asymptotics are interior-only). With covariates the eq. (17) bounds are unconditional-distribution objects and do not apply: `q_lower`/`q_upper` are NaN, no inference overwrite runs (qte applies no guard on its covariate path either), and the conditional-envelope diagnostic below is the support check. + +*Covariates (qte `xformla` parity - the Melly-Santangelo 2015 QR pipeline in qte's simplified form):* `fit(covariates=[...])` or trailing formula terms (`"y ~ treated * post + x1"`). Linear quantile regressions `rq(y ~ X)` are fit in the control pre- and post-period cells on qte's fixed internal tau grid `seq(0.01, 0.99, 0.01)` (99 points, hardcoded in qte's `compute.CiC`, not user-configurable); each treated pre-period observation receives its conditional rank `Fhat_{00|X_i}(Y_i)` and imputed counterfactual `y0t_i = Qhat_{01|X_i}(rank_i)` via quantreg `predict.rqs`'s `Fhat`/`Qhat` step-function conventions (ported verbatim, including their `taus[0]` floor and one-step lag); the counterfactual distribution is the empirical distribution of the imputations, and ATT/QTE follow the unconditional arithmetic with type-1 quantiles. Bootstrap replicates refit every per-cell quantile regression (qte's `bootiter` re-runs the whole estimator), so a covariate fit at `n_bootstrap=200` solves ~40k small LPs - tens of seconds at moderate cell sizes, the same cost profile as `qte::CiC`. +- **Note (integration over treated-PRE covariates):** qte imputes per treated pre-period observation and averages over the treated PRE-period covariate distribution; Melly-Santangelo integrate over the treated POST-period covariates (`F_{X|11}`). For panel data with time-invariant covariates they coincide; for repeated cross-sections with composition change they differ. qte's convention is canonical for diff-diff (locked parity direction). +- **Note (linear QR, numeric covariates, no rearrangement):** covariates enter linearly (dummy-encode categoricals - non-numeric columns raise, unlike R's silent `model.matrix` factor expansion); the `Fhat`/`Qhat` step functions use the raw (un-rearranged) QR process, so under quantile crossing the imputation map can be non-monotone - Melly-Santangelo would monotonize, qte does not. Monotone-transform equivariance does NOT extend to the covariate branch (linear-in-covariates QR is not equivariant to nonlinear monotone transforms). +- **Note (conditional-envelope support diagnostic; CiC only):** warns when more than 10% (`_ENVELOPE_SHARE_WARN`, analogous to the ties heuristic) of treated pre-period outcomes fall outside their per-observation conditional quantile envelope - the min/max SPAN of the 99 predicted control pre-period grid quantiles at the observation's covariates, which is exactly the region where the Fhat rank is an extrapolated floor/ceiling plateau (under quantile crossing the span can exceed the tau=0.01/0.99 endpoint columns; the span is the predicate because it matches the step-function semantics). This is the Melly-Santangelo Assumption-4 support/overlap analogue of Athey-Imbens Assumption 3.4. ~2% outside is expected under correct specification (the envelope spans taus 0.01-0.99). Scope is deliberately the rank cell only (c00); the post-period `Qhat` lookup is not separately checked. +- **Note (QDiD monotonicity check moot under covariates):** the footnote-21 warning diagnoses the unconditional additive quantile model; the covariate-path `q0` is a type-1 quantile of the imputed sample and monotone by construction, so the check is skipped on covariate fits. +- **Note (NA handling deviates from qte):** qte silently `na.omit`s inside `rq`; diff-diff extends its fit-level dropna-with-warning to covariate columns and rejects non-finite covariate values, so no row is silently dropped inside the quantile regressions. +- **Deviation from R (solver + exact-tie selection):** the quantile regressions solve the Koenker-Bassett LP with scipy's HiGHS instead of quantreg's Barrodale-Roberts simplex. Both return exact vertex solutions and agree to ~1e-14 when the optimum is unique, but two exact-tie artifacts make bit-level end-to-end agreement unattainable: (i) the check loss can have a non-trivial optimal face (structural with binary covariates), where the two solvers legitimately return different exact minimizers; (ii) R's predicted `Fhat` knots are often EXACTLY tied across adjacent taus (shared interpolating basis) and `predict.rqs` orders ties by tau index, while Python's ~1e-15-different predictions break those ties by value, so a share of conditional ranks lands one grid step away. Both are selections among equally valid solutions. Parity is therefore layered: the step-function conventions and both compositions are pinned BIT-EXACTLY conditioned on R's stored coefficients/predictions (`qr_cases` fixtures), the LP solver is proven exactly optimal at every tau (equal coefficients OR equal check loss to 1e-10), and end-to-end covariate results are gated at empirically calibrated tie-selection bounds (ATT 0.04, per-quantile QTE 0.25). The committed fixtures measure worst 9.3e-3 / 8.1e-2 on the generation platform; a 30-dataset randomized decomposition audit (5 DGP families incl. binary-covariate and ties-heavy cells, 2026-07-13) observed same-mechanism deviations up to 3.5e-2 / 0.40 on adversarial data and verified the full attribution story (composition-on-R-coefficients exact 30/30; 8,910 LP solves all coefficient-matching or two-sided loss-equal; all 1,586 rank flips attributed; no directional bias) - the gates carry cross-platform margin because a different BLAS can flip different ties on the same fixture data, while a real composition bug errs at O(0.5-1). Sharpness floors additionally require most CiC quantiles (>= 10 of 19) and at least one QDiD quantile to match R within 1e-8. +- **Note (QDiD covariate branch, asymmetric quantile types):** qte's covariate QDiD fits QR in THREE cells (both control cells plus treated-pre), takes conditional ranks from the treated pre-period cell's OWN conditional distribution, imputes additively `y0t_i = Y_i + Qhat_{01|X_i}(rank_i) - Qhat_{00|X_i}(rank_i)`, and then mixes quantile types: `q1 = Q7(y11, tau)` (R's default type-7) but `q0 = Q1(y0t, tau)` (type-1 via `quantile.ecdf`). This asymmetry is a qte 1.3.1 implementation fact, ported verbatim. Own-cell ranks make QDiD's covariate path structurally knot-exact (the c10 QR interpolates its own evaluation points), which is why its tie-selection deviations run larger than CiC's. *Standard errors:* - Bootstrap-only in v1 (`n_bootstrap=200` default, `seed` param; `n_bootstrap=0` -> joint-NaN inference via `safe_inference`/`safe_inference_batch`). @@ -3806,19 +3815,19 @@ where `Q1` is the R type-1 quantile - the paper's eq. (35)/(A.1) inf-based ceili - Bootstrap with < 50% finite replicate rows -> all SEs and `sup_t_crit` NaN + failure-rate warning (`warn_bootstrap_failure_rate`). **Deferred (reviewed, documented - see the paper-review docs for full scoping):** -- Covariates (paper Section 5.1 routes; Melly-Santangelo 2015 QR pipeline is the modern reference). `fit()` accepts no covariates; formula covariate terms raise `ValueError`. +- The FULL Melly-Santangelo (2015) covariate estimator: monotonized integrated-indicator conditional CDFs (CFG 2010 rearrangement), integration over the treated post-period covariates (`F_{X|11}`), exchangeable bootstrap with variance-weighted KS uniform bands, tail trimming, and the pre-period time-invariance specification test. The implemented covariate branch above is qte's simplified form of the same pipeline. The Athey-Imbens Section 5.1 parametric residual route (OLS residualization) is also not implemented. - Discrete-outcome bounds and DCIC point identification (Sections 4, 5.2; Kranker's Stata `cic` implements them). - Analytical SEs (Theorems 5.1-5.3 influence functions; panel Theorems 5.5-5.7; Appendix B covariances). - Multiple groups/periods (Section 6) - `ecic` (R) is the staggered event-study CiC lineage; Ciaccio's copula-based staggered distributional DiD is a distinct method (reviewed separately, do not conflate). - Treatment on the controls (Theorem 3.2, group-label exchange). **Reference implementation(s):** -- R: `qte::CiC()` (v1.3.1, pinned) - the parity target. Golden fixtures: `benchmarks/R/generate_qte_golden.R` -> `benchmarks/data/qte_golden.json`; parity tests: `tests/test_changes_in_changes_parity.py` (point ATT/QTE at atol=1e-10 across 4 scenarios x panel/RCS, type-1 micro-fixtures at atol=0, seeded-R SE block compared statistically). +- R: `qte::CiC()` (v1.3.1, pinned; quantreg 6.1 pinned too - the covariate fixtures embed quantreg's `rq`/`predict.rqs` behavior). Golden fixtures: `benchmarks/R/generate_qte_golden.R` -> `benchmarks/data/qte_golden.json`; parity tests: `tests/test_changes_in_changes_parity.py` (unconditional point ATT/QTE at atol=1e-10 across 4 scenarios x panel/RCS, type-1 micro-fixtures at atol=0; covariate conventions at atol=0 via the `qr_cases` block conditioned on R's stored coefficients/predictions, LP optimality per tau, end-to-end covariate results at the tie-selection bounds documented above; seeded-R SE blocks compared statistically - generated with `cores=1`, since qte's default `cores=2` forks the bootstrap and is NOT reproducible even under `set.seed`). - Stata: `cic` (Kranker) - analytical SEs + discrete bounds (deferred scope). ### QDiD (quantile DiD comparison estimator) -**Primary source:** Athey & Imbens (2006), Section 3.3 (pp. 446-447) - the paper formalizes QDiD as the natural comparison estimator and recommends CiC over it: QDiD's justifying model (eq. 22) is not invariant to monotone rescaling of the outcome, forces `U ⊥ (G, T)` (identical unobservable distributions in all four cells), and places testable restrictions on the data (footnote 21 - the implementation warns when the implied counterfactual quantile function is non-monotone on the requested grid). +**Primary source:** Athey & Imbens (2006), Section 3.3 (pp. 446-447) - the paper formalizes QDiD as the natural comparison estimator and recommends CiC over it: QDiD's justifying model (eq. 22) is not invariant to monotone rescaling of the outcome, forces `U ⊥ (G, T)` (identical unobservable distributions in all four cells), and places testable restrictions on the data (footnote 21 - the implementation warns when the implied counterfactual quantile function is non-monotone on the requested grid; unconditional fits only, see the covariates block above). *Estimator equations (as implemented, matching `qte::QDiD()` v1.3.1 exactly):* @@ -3828,6 +3837,7 @@ where `Q1` is the R type-1 quantile - the paper's eq. (35)/(A.1) inf-based ceili where `Q7` is the R default type-7 (linear-interpolation) quantile (numpy `method="linear"`). - **Note (finite-sample deviation from the paper's k^QDID transformation):** the paper's p. 447 display defines QDiD through the transformation `k^QDID(y) = y + F_01^{-1}(F_10(y)) - F_00^{-1}(F_10(y))`; qte 1.3.1 instead computes the additive quantile-DiD above with type-7 quantiles, and its ATT evaluates the control-group quantile functions at the treated pre-period's own-sample ranks. The two constructions are population-equivalent (and QDiD's mean effect equals standard DiD's ATT under continuity, p. 447 - locked as a large-N tolerance test) but are different finite-sample estimators. diff-diff implements the qte form as canonical per the locked parity direction (2026-07-12); the paper-exact transformation form is not implemented. - No asymptotic theory for QDiD exists in the paper; bootstrap inference (identical machinery to CiC above) is a library/qte convention. No interior-range guard applies (eq. 17 has no QDiD analogue): `q_lower`/`q_upper` are NaN on QDiD results. +- Covariates: see the CiC covariates block above (three-cell QR, own-cell conditional ranks, additive imputation, and the verbatim-ported asymmetric Q7/Q1 quantile-type pair). **Reference implementation(s):** - R: `qte::QDiD()` (v1.3.1, pinned) - same fixture/test infrastructure as CiC. diff --git a/docs/methodology/papers/athey-imbens-2006-review.md b/docs/methodology/papers/athey-imbens-2006-review.md index dae82bc7..3be9cabb 100644 --- a/docs/methodology/papers/athey-imbens-2006-review.md +++ b/docs/methodology/papers/athey-imbens-2006-review.md @@ -345,15 +345,16 @@ Aggregation `tau_Lambda = Lambda' tau^CIC_I` (columns of Lambda sum to 1): sampl **Planned diff-diff v1 scope (2026-07-12):** - Ship: `ChangesInChanges` + `QDiD`, 2x2 design, continuous outcomes, bootstrap inference, panel + repeated cross-section modes. -- Deferred: covariates (Theorems 4.3-4.4 and the Section 5.1 residual route; Melly-Santangelo is the modern reference), discrete-outcome bounds (Section 4 identification / Section 5.2 inference, incl. Imbens-Manski intervals), analytical SEs (Theorems 5.1-5.3, 5.5-5.7; Appendix B covariances), multiple groups/periods (Section 6; `ecic` for the staggered event-study CiC lineage - Ciaccio's copula-based staggered distributional DiD is a distinct method, reviewed separately). +- Deferred: discrete-outcome bounds (Section 4 identification / Section 5.2 inference, incl. Imbens-Manski intervals), analytical SEs (Theorems 5.1-5.3, 5.5-5.7; Appendix B covariances), multiple groups/periods (Section 6; `ecic` for the staggered event-study CiC lineage - Ciaccio's copula-based staggered distributional DiD is a distinct method, reviewed separately). - All deferred material is reviewed in this document so the deferral is documented, not silent. +- **Update (2026-07-13, covariates PR):** covariates now SHIP via qte 1.3.1's `xformla` branch (per-cell linear quantile regression - the Melly-Santangelo pipeline in qte's simplified form; see the REGISTRY covariates block). What remains deferred on the covariate front: the paper's Theorems 4.3-4.4 bound route, the Section 5.1 parametric residual route, and the full Melly-Santangelo estimator (monotonized CDFs, treated-post integration, exchangeable bootstrap). ### Data Structure Requirements - Long format: one row per observation with outcome, group indicator (0/1), and period indicator (0/1); all four (g,t) cells non-empty (Assumption 5.1(ii)). - Repeated cross-section mode: four independent samples, one per cell, sizes `N_00, N_01, N_10, N_11` (Assumption 5.1(i)). - Panel mode: unit identifier required; each unit observed in both periods; per-group iid draws of the pair `(Y_i0, Y_i1)` (Assumption 5.3); the point estimator ignores the pairing (identical to repeated cross-section), but inference must respect it (resample units). - Continuous outcomes; heavy ties trigger the discrete-outcome warning. -- Covariates: not accepted in v1 (deferred routes documented above). +- Covariates: numeric covariates accepted since the covariates PR (qte `xformla` parity route; dummy-encode categoricals). The Section 5.1 residual route and Theorems 4.3-4.4 bounds remain unimplemented. ### Computational Considerations - Sorting the four cells dominates: O(N log N). The transformation `F_hat_01^{-1}(F_hat_00(y))` evaluates via two `searchsorted` passes over sorted arrays - O(N_10 log N) for all treated-pre points. Memory: O(N). diff --git a/docs/methodology/papers/melly-santangelo-2015-review.md b/docs/methodology/papers/melly-santangelo-2015-review.md index 61b025be..0c0e6ede 100644 --- a/docs/methodology/papers/melly-santangelo-2015-review.md +++ b/docs/methodology/papers/melly-santangelo-2015-review.md @@ -13,7 +13,7 @@ ## CiC with Covariates (Melly-Santangelo) -**Status: NOT shipping in diff-diff CiC/QDiD v1 (scope decision 2026-07-12).** Covariates are explicitly deferred from v1; this review documents the covariate extension so the deferral is informed and any future covariates PR starts from a reviewed scope. +**Status: the qte-style SIMPLIFIED form of this pipeline SHIPPED in the covariates PR (2026-07-13)** - per-cell linear quantile regressions on qte's fixed 99-tau grid, raw (un-rearranged) `predict.rqs` conditional CDF/quantile step functions, per-observation imputation integrated over the treated PRE-period covariates, and qte's bootstrap. The FULL estimator this paper develops (monotonized integrated-indicator CDFs, `F_{X|11}` treated-post integration, exchangeable bootstrap with variance-weighted KS bands, tail trimming, specification test) remains unimplemented; this review is its scoped reference. **Primary source:** Melly, B., & Santangelo, G. (2015). The Changes-in-Changes Model with Covariates. *Working paper*, Bern University and European Commission - JRC. Distributed via https://sites.google.com/site/blaisemelly/home/computer-programs/cic_stata. - Layout: Sections 1 (Introduction, pp. 2-3), 2 (Model and identification, pp. 4-9), 3 (Estimation, pp. 10-12), 4 (Asymptotic results, pp. 12-21; 4.4 "Inference" pp. 19-21 includes the time-invariance specification test), 5 (Application, pp. 21-24), 6 (Conclusion, pp. 24-25); Figures 1-5 on pp. 26-30; References pp. 31-32. @@ -437,9 +437,9 @@ All quantiles and covariate values must be considered to detect deviations; Kolm - Stata: Blaise Melly's website (https://sites.google.com/site/blaisemelly/home/computer-programs/cic_stata) distributes a Stata implementation accompanying this paper. The paper's Conclusion says only "we provide codes that implement the estimation and inference procedures developed in this paper" (p. 24) and names no command on the reviewed pages - the command-name attribution comes from the website, not the paper. - **IMPORTANT disambiguation:** this is DISTINCT from Kranker's SSC `cic` Stata module, which implements Athey-Imbens 2006 (unconditional CiC with analytical SEs and discrete bounds). Verify the provenance of any Stata artifact before using it as a parity reference in a future covariates PR. -- No R implementation is known to the initiative for covariate CiC. A future PR would need simulation-based validation - this motivated deferring covariates from v1. +- ~~No R implementation is known to the initiative for covariate CiC.~~ **Correction (2026-07-13):** this was wrong - qte 1.3.1's `CiC()`/`QDiD()` support covariates via `xformla`, implementing a simplified form of this paper's QR pipeline (fixed 99-tau grid, per-observation imputation, treated-PRE integration, no monotonization). That branch is now diff-diff's R parity target for covariates (see REGISTRY). The FULL Melly-Santangelo estimator still has no R implementation and would need simulation-based validation. -**Requirements checklist (for the future covariates PR, not v1):** +**Requirements checklist (for a future FULL-Melly-Santangelo PR; the qte simplified branch shipped 2026-07-13 covers none of the rows below except as noted in the validation row):** - [ ] Four per-cell QR coefficient processes on a fine mesh `u_1..u_S` with `delta*sqrt(n) -> 0`; Koenker-Bassett check-function objective per (g,t) cell - [ ] Monotonized conditional CDFs via the integrated-indicator representation (pp. 10-11, CFG 2010); never invert raw QR processes directly - [ ] Conditional CiC composition per eq. (7); covariate integration over the empirical `F_{X|11}` per the p. 12 displays (resolving the printed index/mesh-weight typos); treated side either empirical CDF or symmetric QR-based integration (application uses the latter) @@ -451,17 +451,17 @@ All quantiles and covariate values must be considered to detect deviations; Kolm - [ ] Support diagnostic: test `Y_10x subset Y_00x` (Assumption 4's observable implication); warn and restrict to the overlap if violated - [ ] Staggered adoption: pairwise-period estimates averaged with weights representative of treated units; pooled QR with period and group dummies when covariates are interacted with trends (Section 5 strategy - application-level, no general theorem) - [ ] Error on discrete outcomes (authors advise against; only partial identification) -- [ ] Validation strategy: simulation-based (no R reference exists); verify any Stata parity artifact is Melly's covariate-CiC code, NOT Kranker's SSC `cic` (Athey-Imbens unconditional) +- [ ] Validation strategy for the FULL estimator: simulation-based (no R reference exists for the full pipeline; qte's `xformla` branch anchors only the simplified form and is already parity-tested); verify any Stata parity artifact is Melly's covariate-CiC code, NOT Kranker's SSC `cic` (Athey-Imbens unconditional) --- ## Implementation Notes -**Relevance to diff-diff CiC/QDiD v1 (2026-07-12):** -- (i) v1 ships covariate-free CiC, and this paper's Figures 1-2 bias analysis of DiD (and the analogous concern for unconditional CiC when group compositions differ) is the documented motivation for the covariates deferral being explicit rather than silent - unconditional CiC assumptions can fail when conditional ones hold. -- (ii) The four-step QR pipeline (estimate conditional CDFs by quantile regression for all four group-period cells, apply CiC transformations conditionally, integrate over the treated-group covariate distribution, invert) is the blueprint for a future covariates PR. -- (iii) The exchangeable bootstrap + KS-band machinery is shared with Callaway-Li-Oka 2018 and reinforces the bootstrap-first inference choice for v1. -- (iv) The monotonization/rearrangement step for QR-estimated conditional CDFs is the key extra numerical ingredient v1 does not need. +**Relevance to diff-diff CiC/QDiD (2026-07-12 v1; updated 2026-07-13 covariates PR):** +- (i) This paper's Figures 1-2 bias analysis of DiD (and the analogous concern for unconditional CiC when group compositions differ) is the documented motivation for covariate support - unconditional CiC assumptions can fail when conditional ones hold. v1 shipped covariate-free; the covariates PR shipped the qte-`xformla` simplified form of this paper's pipeline. +- (ii) The four-step QR pipeline (estimate conditional CDFs by quantile regression, apply CiC transformations conditionally, integrate over the treated covariate distribution, invert) IS the implemented blueprint, in qte's simplified form (fixed 99-tau grid, per-observation imputation, treated PRE-period integration, raw un-rearranged step functions). +- (iii) The exchangeable bootstrap + KS-band machinery is shared with Callaway-Li-Oka 2018 and reinforces the bootstrap-first inference choice; the implemented covariate bootstrap is qte's (unit-block / pooled-row), not this paper's exchangeable weights. +- (iv) The monotonization/rearrangement step for QR-estimated conditional CDFs is the key numerical ingredient of the FULL estimator that the implemented qte form deliberately omits (qte uses the raw QR process; documented REGISTRY Note). ### Data Structure Requirements - Repeated cross sections with four (g,t) cells, each with positive probability `alpha_gt` (Assumption 5). Panel data requires modified theory (AI Section 5.3-style within-group over-time correlation terms) that the paper does not develop. @@ -516,7 +516,7 @@ All quantiles and covariate values must be considered to detect deviations; Kolm **Version and coverage gaps:** - **Preliminary working paper.** The reviewed October 2015 draft is flagged "Preliminary!" and contains the printed typos above. All equation/assumption/theorem numbers are pinned to this draft; later drafts, if any, may renumber or repair the typos. The paper remains unpublished as of 2026-07-12. - **No Stata command name in the paper.** The Conclusion (p. 24) says "we provide codes" without naming a command anywhere on the reviewed pages; the command-name attribution rests on Melly's website. Verify provenance before parity use, and do not confuse it with Kranker's SSC `cic` module (Athey-Imbens 2006 unconditional CiC). -- **No R implementation and no Monte Carlo.** The paper reports no simulations (only the analytic DiD-bias illustration, pp. 22-23), and no R implementation of covariate CiC is known to the initiative - a future PR needs simulation-based validation and possibly a purpose-built oracle. +- **No R implementation of the FULL estimator, and no Monte Carlo.** The paper reports no simulations (only the analytic DiD-bias illustration, pp. 22-23). qte 1.3.1's `xformla` branch is an R implementation of the SIMPLIFIED pipeline and shipped as diff-diff's parity-tested covariate route (2026-07-13); the full estimator developed here (monotonized CDFs, treated-post integration, exchangeable bootstrap, uniform bands, specification test) still has no R reference - implementing it would need simulation-based validation and possibly a purpose-built oracle. - **Number of resampling draws unreported.** No B is stated for the application's subsampling (500 of 3,000+ counties per draw, footnote 11; draw count absent). - **Tail spikes unexplained.** Figures 3 and 5 show large QTE spikes at both extremes; the paper offers no boundary-artifact discussion, no quantile-grid trimming, and no tail-truncation rule for the application. The density-in-denominator structure of (11)-(13) is a plausible mechanical explanation but is not drawn by the paper. - **Staggered-adoption aggregation is informal.** The pairwise-averaging weights ("representative of the treated counties") and the pooled-QR-with-dummies device are described in prose (p. 23) without formulas or dedicated asymptotic theory; the formal results cover only the 2x2 case. diff --git a/docs/r_comparison.rst b/docs/r_comparison.rst index d11f8ac4..9cea29bf 100644 --- a/docs/r_comparison.rst +++ b/docs/r_comparison.rst @@ -435,7 +435,9 @@ Feature Comparison Table Continuous DiD is available via the ``did`` package continuous extension; Triple Difference requires manual implementation in R. Changes-in-Changes and QDiD are available via the ``qte`` package - (``qte::CiC()`` / ``qte::QDiD()``, the diff-diff parity target). + (``qte::CiC()`` / ``qte::QDiD()``, the diff-diff parity target - + including covariates: diff-diff's ``covariates=`` ports qte's + ``xformla`` branch, a quantreg-based conditional CiC). TROP and Efficient DiD have no direct R equivalents. HeterogeneousAdoptionDiD (dCDH 2026) overlaps with the dedicated R package ``DIDHAD`` (de Chaisemartin et al., 2025), which covers the diff --git a/docs/references.rst b/docs/references.rst index d9a0663e..ab60721c 100644 --- a/docs/references.rst +++ b/docs/references.rst @@ -299,7 +299,15 @@ Changes-in-Changes / Distributional DiD - **Athey, S., & Imbens, G. W. (2006).** "Identification and Inference in Nonlinear Difference-in-Differences Models." *Econometrica*, 74(2), 431-497. https://doi.org/10.1111/j.1468-0262.2006.00668.x - Primary source for the ``ChangesInChanges`` (alias ``CiC``) and ``QDiD`` estimators: nonlinear DiD recovering the treated group's full counterfactual outcome distribution and quantile treatment effects in the 2x2 design, with the quantile-DiD comparison estimator the paper formalizes alongside it. Point estimation matches the R ``qte`` package (v1.3.1, Callaway) exactly; bootstrap inference follows the same package's conventions. Paper review on file at ``docs/methodology/papers/athey-imbens-2006-review.md``; companion reviews for deferred extensions: ``callaway-li-oka-2018-review.md`` (panel QTT), ``melly-santangelo-2015-review.md`` (covariates), ``ciaccio-2024-review.md`` (staggered). + Primary source for the ``ChangesInChanges`` (alias ``CiC``) and ``QDiD`` estimators: nonlinear DiD recovering the treated group's full counterfactual outcome distribution and quantile treatment effects in the 2x2 design, with the quantile-DiD comparison estimator the paper formalizes alongside it. Point estimation matches the R ``qte`` package (v1.3.1, Callaway) exactly; bootstrap inference follows the same package's conventions. Paper review on file at ``docs/methodology/papers/athey-imbens-2006-review.md``; companion reviews: ``melly-santangelo-2015-review.md`` (covariates - implemented in qte's simplified form, see below), ``callaway-li-oka-2018-review.md`` (panel QTT, deferred), ``ciaccio-2024-review.md`` (staggered, deferred). + +- **Melly, B., & Santangelo, G. (2015).** "The Changes-in-Changes Model with Covariates." *Working paper*, Bern University and European Commission - JRC. https://sites.google.com/site/blaisemelly/home/computer-programs/cic_stata + + Reference for conditional (covariate) changes-in-changes: per-cell quantile-regression conditional CDFs composed into a conditional CiC and integrated over the treated covariate distribution. ``ChangesInChanges``/``QDiD`` implement the simplified form of this pipeline that the R ``qte`` package ships as ``xformla`` (fixed 99-tau grid, per-observation imputation, treated pre-period integration); the paper's full estimator (monotonized CDFs, exchangeable bootstrap, uniform bands, specification test) is documented-deferred. The conditional-support diagnostic warning cites this paper's Assumption 4. Paper review on file at ``docs/methodology/papers/melly-santangelo-2015-review.md``. + +- **Koenker, R., & Bassett, G. (1978).** "Regression Quantiles." *Econometrica*, 46(1), 33-50. https://doi.org/10.2307/1913643 + + Source of the linear quantile-regression check-function objective that the covariate CiC/QDiD branch solves (as a linear program via ``scipy.optimize.linprog``/HiGHS, matching R ``quantreg::rq``'s Barrodale-Roberts solutions; see the solver Note in ``docs/methodology/REGISTRY.md``). Continuous Treatment DiD ------------------------ diff --git a/tests/test_changes_in_changes.py b/tests/test_changes_in_changes.py index e4e6afba..7a1b0a6c 100644 --- a/tests/test_changes_in_changes.py +++ b/tests/test_changes_in_changes.py @@ -38,6 +38,36 @@ def make_2x2(n_treated=60, n_control=80, seed=0, effect=1.0): ) +def make_cov_2x2(n_treated=40, n_control=40, seed=0, effect=1.0, x_shift=0.4): + """2x2 with covariates x1 (relevant, group-shifted composition) and x2 (noise). + + Both the level and the noise scale of y depend on x1, so the conditional + (quantile-regression) path genuinely differs from the unconditional one. + Covariates are time-invariant; cells stay small enough that the ~200 LP + solves per covariate fit keep the suite fast. + """ + rng = np.random.default_rng(seed) + n = n_treated + n_control + treat = np.repeat([1, 0], [n_treated, n_control]) + x1 = rng.uniform(0, 2, n) + x_shift * treat + x2 = rng.normal(1, 0.5, n) + u = rng.normal(0, 1, n) + y_pre = 0.5 + 0.8 * x1 + (0.4 + 0.2 * x1) * u + y_post = ( + 0.8 + 1.1 * x1 + (0.5 + 0.2 * x1) * (0.7 * u + 0.5 * rng.normal(0, 1, n)) + treat * effect + ) + return pd.DataFrame( + { + "id": np.tile(np.arange(n), 2), + "post": np.repeat([0, 1], n), + "treated": np.tile(treat, 2), + "x1": np.tile(x1, 2), + "x2": np.tile(x2, 2), + "y": np.concatenate([y_pre, y_post]), + } + ) + + def fit_quiet(est, df, **kwargs): """Fit while swallowing the (expected, tested-elsewhere) diagnostic warnings.""" with warnings.catch_warnings(): @@ -184,9 +214,41 @@ def test_formula_interaction_order_invariant(self, cls): r_a.quantile_effects["qte"].to_numpy(), r_b.quantile_effects["qte"].to_numpy() ) - def test_formula_covariates_rejected(self, cls): - with pytest.raises(ValueError, match="[Cc]ovariates"): - cls(n_bootstrap=0).fit(make_2x2(), formula="y ~ treated * post + x1") + def test_formula_covariates_equal_kwarg(self, cls): + # Trailing formula terms are covariates and must reproduce the kwarg + # route exactly (both '*' and ':' grammars). + df = make_cov_2x2(seed=5) + r_kw = fit_quiet(cls(n_bootstrap=0), df, covariates=["x1", "x2"]) + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + r_star = cls(n_bootstrap=0).fit(df, formula="y ~ treated * post + x1 + x2") + r_colon = cls(n_bootstrap=0).fit( + df, formula="y ~ treated + post + x1 + x2 + treated:post" + ) + assert r_star.att == r_kw.att == r_colon.att + np.testing.assert_array_equal( + r_star.quantile_effects["qte"].to_numpy(), r_kw.quantile_effects["qte"].to_numpy() + ) + np.testing.assert_array_equal( + r_colon.quantile_effects["qte"].to_numpy(), r_kw.quantile_effects["qte"].to_numpy() + ) + assert r_star.covariates == r_kw.covariates == r_colon.covariates == ["x1", "x2"] + + def test_formula_with_explicit_kwargs_raises(self, cls): + # Uniform strictness (deliberately stricter than DifferenceInDifferences, + # which silently lets the formula win): formula + ANY explicit column + # argument is ambiguous and raises. + df = make_cov_2x2() + with pytest.raises(ValueError, match="not both"): + cls(n_bootstrap=0).fit(df, formula="y ~ treated * post", covariates=["x1"]) + with pytest.raises(ValueError, match="not both"): + cls(n_bootstrap=0).fit(df, formula="y ~ treated * post", outcome="y") + + def test_colon_form_requires_pair_as_mains(self, cls): + # 'y ~ treated + treated:post + x1' must not silently fall back to + # interaction-order role assignment (v1 role-order invariant). + with pytest.raises(ValueError, match="main effects"): + cls(n_bootstrap=0).fit(make_cov_2x2(), formula="y ~ treated + treated:post + x1") def test_formula_missing_interaction(self, cls): with pytest.raises(ValueError, match="interaction"): @@ -483,6 +545,309 @@ def test_tiny_cells_gate_to_nan(self): assert np.isfinite(res.att) # point estimate unaffected +# ============================================================================= +# Covariates (qte xformla parity path) +# ============================================================================= + + +@BOTH +class TestCovariates: + def test_happy_path(self, cls): + res = fit_quiet(cls(n_bootstrap=0), make_cov_2x2(), covariates=["x1"]) + assert np.isfinite(res.att) + assert np.all(np.isfinite(res.quantile_effects["qte"])) + assert res.covariates == ["x1"] + + def test_covariates_change_the_estimate(self, cls): + # x1 is genuinely relevant (group-shifted composition), so the + # conditional and unconditional estimates must differ. + df = make_cov_2x2(seed=8) + r_cov = fit_quiet(cls(n_bootstrap=0), df, covariates=["x1"]) + r_unc = fit_quiet(cls(n_bootstrap=0), df) + assert r_cov.att != r_unc.att + + def test_empty_list_behaves_as_none(self, cls): + df = make_cov_2x2(seed=9) + r_empty = fit_quiet(cls(n_bootstrap=0), df, covariates=[]) + r_none = fit_quiet(cls(n_bootstrap=0), df) + assert r_empty.att == r_none.att + assert r_empty.covariates is None + + def test_missing_covariate_column(self, cls): + with pytest.raises(ValueError, match="Covariate column 'age' not found"): + fit_quiet(cls(n_bootstrap=0), make_cov_2x2(), covariates=["age"]) + + def test_bare_string_covariates_rejected(self, cls): + # covariates="x1" would iterate character-wise (["x", "1"]) and could + # silently fit the wrong covariate set if such columns exist. + with pytest.raises(ValueError, match="bare string"): + fit_quiet(cls(n_bootstrap=0), make_cov_2x2(), covariates="x1") + + def test_non_numeric_covariate(self, cls): + df = make_cov_2x2() + df["region"] = np.where(df["x1"] > 1, "north", "south") + with pytest.raises(ValueError, match="dummy-encode"): + fit_quiet(cls(n_bootstrap=0), df, covariates=["region"]) + + def test_reserved_name_collision(self, cls): + with pytest.raises(ValueError, match="collide"): + fit_quiet(cls(n_bootstrap=0), make_cov_2x2(), covariates=["y"]) + + def test_duplicate_covariates(self, cls): + with pytest.raises(ValueError, match="duplicate"): + fit_quiet(cls(n_bootstrap=0), make_cov_2x2(), covariates=["x1", "x1"]) + + def test_na_in_covariate_rows_dropped_with_warning(self, cls): + df = make_cov_2x2() + df.loc[:2, "x1"] = np.nan + with pytest.warns(UserWarning, match="Dropped 3 row"): + with warnings.catch_warnings(): + warnings.simplefilter("always") + res = cls(n_bootstrap=0).fit( + df, outcome="y", treatment="treated", time="post", covariates=["x1"] + ) + assert res.n_obs == len(df) - 3 + + @pytest.mark.parametrize("bad", [np.inf, -np.inf]) + def test_nonfinite_covariate_raises(self, cls, bad): + df = make_cov_2x2() + df.loc[3, "x1"] = bad + with pytest.raises(ValueError, match="non-finite"): + fit_quiet(cls(n_bootstrap=0), df, covariates=["x1"]) + + def test_too_few_obs_per_qr_cell(self, cls): + # 3 rows in a quantile-regression cell with 2 covariates: below k+2. + rng = np.random.default_rng(0) + df = pd.DataFrame( + { + "post": [0, 0, 0, 1, 1, 1] * 2, + "treated": [0] * 6 + [1] * 6, + "x1": rng.normal(0, 1, 12), + "x2": rng.normal(0, 1, 12), + "y": rng.normal(0, 1, 12), + } + ) + with pytest.raises(ValueError, match="Too few observations for quantile regression"): + fit_quiet(cls(n_bootstrap=0), df, covariates=["x1", "x2"]) + + def test_constant_covariate_runs_finite(self, cls): + # Zero-variance covariate: the LP still solves (collinear direction is + # flat, on-span predictions stay determined - qte/rq behave the same); + # no proactive error, results finite. + df = make_cov_2x2(seed=10) + df["x1"] = 1.5 + res = fit_quiet(cls(n_bootstrap=0), df, covariates=["x1"]) + assert np.isfinite(res.att) + assert np.all(np.isfinite(res.quantile_effects["qte"])) + + def test_interaction_order_invariant_with_covariates(self, cls): + df = make_cov_2x2(seed=11) + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + r_a = cls(n_bootstrap=0).fit(df, formula="y ~ treated + post + x1 + treated:post") + r_b = cls(n_bootstrap=0).fit(df, formula="y ~ treated + post + x1 + post:treated") + assert r_a.att == r_b.att + assert r_a.covariates == r_b.covariates == ["x1"] + + def test_panel_and_rcs_identical_points(self, cls): + # Panel mode changes only the bootstrap, never the point estimate - + # also true on the covariate path (qte parity: identical ate observed + # in the golden generator for cov scenarios). + df = make_cov_2x2(seed=12) + r_panel = fit_quiet(cls(n_bootstrap=0, panel=True), df, unit="id", covariates=["x1"]) + r_rcs = fit_quiet(cls(n_bootstrap=0), df, covariates=["x1"]) + assert r_panel.att == r_rcs.att + np.testing.assert_array_equal( + r_panel.quantile_effects["qte"].to_numpy(), + r_rcs.quantile_effects["qte"].to_numpy(), + ) + + def test_seed_determinism_with_covariates(self, cls): + df = make_cov_2x2(n_treated=25, n_control=25, seed=13) + r1 = fit_quiet(cls(n_bootstrap=12, seed=7), df, covariates=["x1"]) + r2 = fit_quiet(cls(n_bootstrap=12, seed=7), df, covariates=["x1"]) + assert r1.se == r2.se + np.testing.assert_array_equal( + r1.quantile_effects["se"].to_numpy(), r2.quantile_effects["se"].to_numpy() + ) + + def test_treated_post_covariates_unused(self, cls): + # Neither estimator consumes x11 (CiC: QR in c00/c01, ranks at c10; + # QDiD: QR in c00/c01/c10): perturbing treated post-period covariate + # values must leave everything unchanged. Locks the cells contract. + df = make_cov_2x2(seed=14) + r_base = fit_quiet(cls(n_bootstrap=0), df, covariates=["x1"]) + df2 = df.copy() + mask = (df2["treated"] == 1) & (df2["post"] == 1) + df2.loc[mask, "x1"] = df2.loc[mask, "x1"] + 100.0 + r_pert = fit_quiet(cls(n_bootstrap=0), df2, covariates=["x1"]) + assert r_base.att == r_pert.att + np.testing.assert_array_equal( + r_base.quantile_effects["qte"].to_numpy(), + r_pert.quantile_effects["qte"].to_numpy(), + ) + + def test_time_varying_covariates_panel_ok(self, cls): + # Each (group, period) cell uses its own rows' covariate values (qte + # parity), so time-varying covariates are legal in panel mode. + df = make_cov_2x2(seed=15) + rng = np.random.default_rng(15) + post_mask = df["post"] == 1 + df.loc[post_mask, "x1"] = df.loc[post_mask, "x1"] + rng.normal(0, 0.2, post_mask.sum()) + res = fit_quiet(cls(n_bootstrap=0, panel=True), df, unit="id", covariates=["x1"]) + assert np.isfinite(res.att) + + def test_bootstrap_failure_gate_with_covariates(self, cls): + # Tiny cells + pooled RCS resampling: replicates frequently empty a + # cell or degenerate the QR - they must NaN-row through the gate, + # never raise. + rng = np.random.default_rng(1) + df = pd.DataFrame( + { + "post": [0, 0, 0, 1, 1, 1] * 2, + "treated": [0] * 6 + [1] * 6, + "x1": rng.normal(0, 1, 12), + "y": rng.normal(0, 1, 12), + } + ) + with warnings.catch_warnings(record=True) as rec: + warnings.simplefilter("always") + res = cls(n_bootstrap=100, seed=0).fit( + df, outcome="y", treatment="treated", time="post", covariates=["x1"] + ) + assert np.isfinite(res.att) + assert res.n_bootstrap_valid <= 100 + del rec # gate warning may or may not fire depending on failure share + + def test_no_bootstrap_nan_inference_with_covariates(self, cls): + res = fit_quiet(cls(n_bootstrap=0), make_cov_2x2(), covariates=["x1"]) + assert np.isfinite(res.att) + assert_nan_inference( + { + "se": res.se, + "t_stat": res.t_stat, + "p_value": res.p_value, + "conf_int": res.conf_int, + } + ) + + def test_results_surface(self, cls): + res = fit_quiet(cls(n_bootstrap=0), make_cov_2x2(), covariates=["x1", "x2"]) + assert res.covariates == ["x1", "x2"] + assert res.to_dict()["covariates"] == ["x1", "x2"] + assert "Covariates: x1, x2" in res.summary() + # Unconditional fits keep covariates=None (and no summary line). + res_unc = fit_quiet(cls(n_bootstrap=0), make_cov_2x2()) + assert res_unc.covariates is None + assert res_unc.to_dict()["covariates"] is None + assert "Covariates:" not in res_unc.summary() + + def test_get_params_has_no_covariates_key(self, cls): + # covariates is fit-scope (like unit), not a hyperparameter. + assert "covariates" not in cls().get_params() + + def test_sklearn_clone_then_fit_with_covariates(self, cls): + sklearn_base = pytest.importorskip("sklearn.base") + est = cls(n_bootstrap=0) + clone = sklearn_base.clone(est) + res = fit_quiet(clone, make_cov_2x2(), covariates=["x1"]) + assert np.isfinite(res.att) + + +class TestCovariateDiagnostics: + def test_no_unconditional_guard_under_covariates(self): + # LOCK TEST for the NaN-exterior hazard: with covariates, + # q_lower/q_upper are NaN and the unconditional interior-range + # overwrite must NOT run - otherwise the all-True NaN mask would + # silently NaN every quantile's inference. + df = make_cov_2x2(n_treated=30, n_control=30, seed=16) + with warnings.catch_warnings(record=True) as rec: + warnings.simplefilter("always") + res = ChangesInChanges(n_bootstrap=20, seed=2).fit( + df, outcome="y", treatment="treated", time="post", covariates=["x1"] + ) + assert np.isnan(res.q_lower) and np.isnan(res.q_upper) + assert not any("interior range" in str(w.message) for w in rec) + qe = res.quantile_effects + assert qe["se"].notna().all() + assert qe["t_stat"].notna().all() + assert qe["p_value"].notna().all() + + def test_envelope_warning_fires_on_support_failure(self): + # Treated pre-period outcomes shifted far outside the conditional + # control pre-period distribution -> MS Assumption-4 diagnostic. + df = make_cov_2x2(seed=17) + mask = (df["treated"] == 1) & (df["post"] == 0) + df.loc[mask, "y"] = df.loc[mask, "y"] + 25.0 + with pytest.warns(UserWarning, match="conditional quantile envelope"): + ChangesInChanges(n_bootstrap=0).fit( + df, outcome="y", treatment="treated", time="post", covariates=["x1"] + ) + + def test_unconditional_support_warning_not_used_under_covariates(self): + # The same shifted DGP must NOT emit the unconditional Assumption-3.4 + # message on the covariate path (the conditional diagnostic replaces it). + df = make_cov_2x2(seed=17) + mask = (df["treated"] == 1) & (df["post"] == 0) + df.loc[mask, "y"] = df.loc[mask, "y"] + 25.0 + with warnings.catch_warnings(record=True) as rec: + warnings.simplefilter("always") + ChangesInChanges(n_bootstrap=0).fit( + df, outcome="y", treatment="treated", time="post", covariates=["x1"] + ) + messages = [str(w.message) for w in rec] + # "Corollary 3.1" appears only in the UNCONDITIONAL support warning + # (the conditional-envelope message references Assumption 3.4 as its + # analogue, so matching on that string would be ambiguous). + assert not any("Corollary 3.1" in m for m in messages) + assert any("conditional quantile envelope" in m for m in messages) + + def test_clean_covariate_fit_no_unexpected_warnings(self): + # Full-overlap covariate DGP (no group composition shift): none of the + # diagnostics may fire. ~2% envelope share is expected by construction; + # the 10% threshold must not be trigger-happy on clean data. + df = make_cov_2x2(n_treated=150, n_control=150, seed=18, x_shift=0.0) + with warnings.catch_warnings(): + warnings.simplefilter("error") + ChangesInChanges(n_bootstrap=0).fit( + df, outcome="y", treatment="treated", time="post", covariates=["x1"] + ) + + def test_ties_warning_still_fires_with_covariates(self): + df = make_cov_2x2(seed=19) + df["y"] = np.round(df["y"]) + with pytest.warns(UserWarning, match="ties"): + ChangesInChanges(n_bootstrap=0).fit( + df, outcome="y", treatment="treated", time="post", covariates=["x1"] + ) + + def test_qdid_monotonicity_check_skipped_with_covariates(self): + # The unconditional-QDiD footnote-21 warning diagnoses the additive + # quantile model, which is not the object estimated under covariates + # (the imputed counterfactual's quantile curve is monotone by + # construction) - it must not fire. + rng = np.random.default_rng(3) + n = 200 + treat = np.repeat([1, 0], n // 2) + x1 = rng.uniform(0, 1, n) + y_pre = np.where(treat == 1, rng.normal(0, 4, n), rng.normal(0, 3, n)) + y_post = np.where(treat == 1, rng.normal(1, 4, n), rng.normal(0.5, 0.1, n)) + df = pd.DataFrame( + { + "post": np.repeat([0, 1], n), + "treated": np.tile(treat, 2), + "x1": np.tile(x1, 2), + "y": np.concatenate([y_pre, y_post]), + } + ) + with warnings.catch_warnings(record=True) as rec: + warnings.simplefilter("always") + QDiD(n_bootstrap=0).fit( + df, outcome="y", treatment="treated", time="post", covariates=["x1"] + ) + assert not any("non-monotone" in str(w.message) for w in rec) + + # ============================================================================= # Results API # ============================================================================= diff --git a/tests/test_changes_in_changes_parity.py b/tests/test_changes_in_changes_parity.py index 9ee633c7..fac86fbd 100644 --- a/tests/test_changes_in_changes_parity.py +++ b/tests/test_changes_in_changes_parity.py @@ -1,15 +1,39 @@ """R-parity golden-fixture tests for ChangesInChanges (CiC) and QDiD vs qte 1.3.1. Fixture: benchmarks/data/qte_golden.json, generated by -benchmarks/R/generate_qte_golden.R (qte pinned to 1.3.1 there and in -benchmarks/R/requirements.R). Point fixtures were generated with se=FALSE and -are deterministic; the se_block was generated seeded (set.seed(42), iters=999) -and is compared statistically since bootstrap draws cannot be replicated -across languages. +benchmarks/R/generate_qte_golden.R (qte pinned to 1.3.1 and quantreg to 6.1 +there and in benchmarks/R/requirements.R). Point fixtures were generated with +se=FALSE and are deterministic; the se_block was generated seeded +(set.seed(42), iters=999, cores=1 - qte's default cores=2 forks and is NOT +reproducible even seeded) and is compared statistically since bootstrap draws +cannot be replicated across languages. Tolerances: -- POINT_ATOL = 1e-10 for end-to-end ATT/QTE values (means and type-7 - interpolation differ from R by ~1 ulp; type-1 selections are bit-exact). +- POINT_ATOL = 1e-10 for unconditional end-to-end ATT/QTE values (means and + type-7 interpolation differ from R by ~1 ulp; type-1 selections are + bit-exact). +- Covariate (xformla) end-to-end values are TIE-SELECTION-BOUNDED, not + ~1e-10: (i) the QR check loss can have a non-trivial optimal face and the + two exact solvers (R br simplex, scipy HiGHS) then select different + vertices with identical loss; (ii) R's Fhat knots are often EXACTLY tied + across adjacent taus (shared interpolating basis) and predict.rqs orders + ties by tau index, while Python's ~1e-15-different predictions break the + ties by value - so a share of conditional ranks legitimately lands a grid + step away. Both are selections among equally valid solutions. Hence: + COV_ATT_ATOL / COV_QTE_ATOL are empirical tie-flip bounds: the committed + fixtures measure worst 9.3e-3 / 8.1e-2 on this platform, and a 30-dataset + randomized decomposition audit (5 DGP families incl. binary-covariate and + ties-heavy cells; 2026-07-13) observed same-mechanism deviations up to + 3.5e-2 / 0.40 on adversarial data - the gates carry cross-platform margin + because a different BLAS can flip DIFFERENT ties on the same fixture data, + while a real composition bug errs at O(0.5-1). The audit's invariants: + composition-on-R-coefficients exact 30/30 (worst 1.3e-15), 8910 LP solves + all coefficient-matching or two-sided loss-equal (worst gap 3.2e-16, zero + descent-probe failures), all 1586 rank flips attributed to the documented + tie mechanisms, no directional bias (sign tests p=0.69/0.59). The + CONVENTIONS are pinned at atol=0 by the qr_cases tests conditioned on R's + stored coefficients/predictions, and the LP solver is proven exactly + optimal per tau (equal coefficients OR equal check loss). - Micro-fixtures: type-1 quantiles at atol=0 (pure order-statistic selection, bit-exact cross-language); type-7 at rtol=1e-15 (R's (1-h)*lo + h*hi vs numpy's lerp differ in the last ulp). @@ -27,12 +51,23 @@ import pytest from diff_diff import ChangesInChanges, QDiD -from diff_diff.changes_in_changes import _quantile_type1, _quantile_type7 +from diff_diff.changes_in_changes import ( + _QR_TAU_GRID, + _design_matrix, + _fhat_eval, + _qhat_eval, + _quantile_type1, + _quantile_type7, + _rq_fit, +) FIXTURE_PATH = Path(__file__).parent.parent / "benchmarks" / "data" / "qte_golden.json" POINT_ATOL = 1e-10 +COV_ATT_ATOL = 0.04 +COV_QTE_ATOL = 0.25 QTE_VERSION = "1.3.1" +QUANTREG_VERSION = "6.1" def _load_fixture(): @@ -61,7 +96,7 @@ def _scenario_df(scenario): return pd.DataFrame({k: scenario["data"][k] for k in ("id", "period", "treat", "y")}) -def _fit(df, method, mode, probs, n_bootstrap=0, seed=None): +def _fit(df, method, mode, probs, n_bootstrap=0, seed=None, covariates=None): cls = ChangesInChanges if method == "cic" else QDiD est = cls( quantiles=probs, @@ -77,12 +112,39 @@ def _fit(df, method, mode, probs, n_bootstrap=0, seed=None): treatment="treat", time="period", unit="id" if mode == "panel" else None, + covariates=covariates, ) +def _as_list(x): + """jsonlite auto_unbox collapses single-element vectors to scalars.""" + return x if isinstance(x, list) else [x] + + def test_metadata_versions_match(fixture): - """The committed fixture must have been generated against the pinned qte.""" + """The committed fixture must match the pinned qte AND quantreg versions + (the covariate fixtures embed quantreg's rq/predict.rqs behavior).""" assert fixture["metadata"]["qte_version"] == QTE_VERSION + assert fixture["metadata"]["quantreg_version"] == QUANTREG_VERSION + + +def test_metadata_cov_gates_match(fixture): + """The fixture's self-described covariate gates stay in sync with this file.""" + assert fixture["metadata"]["cov_att_atol"] == COV_ATT_ATOL + assert fixture["metadata"]["cov_qte_atol"] == COV_QTE_ATOL + + +def test_qr_tau_grid_is_r_seq_bit_exact(fixture): + """_QR_TAU_GRID is pinned to R's exact seq(0.01, 0.99, 0.01) doubles. + + Natural numpy constructions differ at 15-25 of the 99 indices by one ulp, + the Fhat rank values are drawn FROM this grid, and the Qhat knots sit ON + it - exact-knot searchsorted lookups are ulp-sensitive (same lesson as + _DEFAULT_QUANTILES). + """ + np.testing.assert_array_equal( + _QR_TAU_GRID, np.asarray(fixture["metadata"]["qr_taus"], dtype=float) + ) def test_default_grid_is_r_seq_bit_exact(fixture, probs): @@ -140,6 +202,194 @@ def test_point_parity(fixture, probs, scenario_name, result_key): ) +SCENARIOS_COV = ["cov1_2x2_n300", "cov2_2x2_n240"] +RESULT_KEYS_COV = ["cic_cov_panel", "cic_cov_rcs", "qdid_cov_panel", "qdid_cov_rcs"] + + +@pytest.mark.parametrize("scenario_name", SCENARIOS_COV) +@pytest.mark.parametrize("result_key", RESULT_KEYS_COV) +def test_covariate_point_parity(fixture, probs, scenario_name, result_key): + """End-to-end covariate parity at the tie-selection bounds (see header).""" + scenario = fixture["scenarios"].get(scenario_name) + if scenario is None: + pytest.skip(f"Scenario {scenario_name} missing from fixture") + golden = scenario["results"][result_key] + method, _, mode = result_key.split("_") + covariates = _as_list(scenario["covariates"]) + df = pd.DataFrame({k: scenario["data"][k] for k in scenario["data"]}) + + res = _fit(df, method, mode, probs, covariates=covariates) + + np.testing.assert_allclose(res.att, golden["ate"], atol=COV_ATT_ATOL, rtol=0) + qte_diff = np.abs( + res.quantile_effects["qte"].to_numpy() - np.asarray(golden["qte"], dtype=float) + ) + np.testing.assert_allclose(qte_diff, 0.0, atol=COV_QTE_ATOL, rtol=0) + # Sharpness floor: tie-selection only perturbs the handful of quantiles + # whose rank lookup lands in a knot-tie group / degenerate-vertex region - + # the REST must agree with R at machine precision. Measured tight counts + # (this platform): CiC 17 and 15 of 19; QDiD 6 and 3 of 19 (own-cell ranks + # are structurally knot-exact, so QDiD flips more; the randomized audit + # confirms QDiD flips heavily on adversarial data). Floors sit well below + # measured so a different BLAS/HiGHS build flipping different ties cannot + # fail them, while a real composition bug (all 19 off) still does. + min_tight = 10 if method == "cic" else 1 + n_tight = int(np.sum(qte_diff < 1e-8)) + assert n_tight >= min_tight, ( + f"{scenario_name}/{result_key}: only {n_tight}/19 quantiles within 1e-8 of R " + f"(sharpness floor {min_tight}) - deviations are broader than tie-selection." + ) + assert res.covariates == covariates + assert np.isnan(res.q_lower) and np.isnan(res.q_upper) + + +class TestQRCases: + """Convention anchors for the covariate branch, conditioned on R's stored + intermediates so tie-selection cannot blur them (see the header docstring): + the Fhat/Qhat step-function lookups and both estimators' compositions must + be BIT-EXACT given R's stored prediction matrices, the dot products must + match R's to the last ulp given R's coefficients, and the LP solver must + be exactly optimal at every tau. + """ + + @staticmethod + def _cells(case): + covs = _as_list(case["covariates"]) + out = {} + for ck, blk in case["cells"].items(): + out[ck] = { + "y": np.asarray(blk["y"], dtype=float), + "X": np.column_stack([np.asarray(blk[c], dtype=float) for c in covs]), + } + return out + + def test_predictions_match_given_r_coefficients(self, fixture): + # Isolates dot-product arithmetic from step-function conventions. + # Last-ulp tolerance rather than exact equality: 2-3-term dot products + # are observed bit-identical to R on this platform, but BLAS/FMA + # reassociation can differ by an ulp elsewhere (assert_allclose + # convention for float linalg). The step-function convention tests + # below stay exact - they consume R's STORED prediction matrices, so + # no floating-point arithmetic sits between fixture and assertion. + for name, case in fixture["qr_cases"].items(): + cells = self._cells(case) + x10d = _design_matrix(cells["c10"]["X"]) + for cell_key, pred_key in ( + ("c00", "preds00_at_x10"), + ("c01", "preds01_at_x10"), + ("c10", "preds10_at_x10"), + ): + coef_r = np.asarray(case[f"coef{cell_key[1:]}"], dtype=float) + np.testing.assert_allclose( + x10d @ coef_r, + np.asarray(case[pred_key], dtype=float), + rtol=1e-15, + atol=1e-300, + err_msg=f"prediction mismatch in '{name}' cell {cell_key}", + ) + + def test_fhat_qhat_conventions_bit_exact_given_r_predictions(self, fixture): + # The predict.rqs Fhat/Qhat step-function ports and both compositions, + # conditioned on R's prediction matrices: atol=0. + for name, case in fixture["qr_cases"].items(): + cells = self._cells(case) + y10 = cells["c10"]["y"] + preds = { + k: np.asarray(case[k], dtype=float) + for k in ("preds00_at_x10", "preds01_at_x10", "preds10_at_x10") + } + ranks_cic = _fhat_eval(preds["preds00_at_x10"], _QR_TAU_GRID, y10) + np.testing.assert_array_equal( + ranks_cic, + np.asarray(case["cic_ranks"], dtype=float), + err_msg=f"CiC ranks mismatch in '{name}'", + ) + y0t_cic = _qhat_eval(preds["preds01_at_x10"], _QR_TAU_GRID, ranks_cic) + np.testing.assert_array_equal( + y0t_cic, + np.asarray(case["cic_y0t"], dtype=float), + err_msg=f"CiC imputations mismatch in '{name}'", + ) + ranks_qdid = _fhat_eval(preds["preds10_at_x10"], _QR_TAU_GRID, y10) + np.testing.assert_array_equal( + ranks_qdid, + np.asarray(case["qdid_ranks"], dtype=float), + err_msg=f"QDiD ranks mismatch in '{name}'", + ) + y0t_qdid = ( + y10 + + _qhat_eval(preds["preds01_at_x10"], _QR_TAU_GRID, ranks_qdid) + - _qhat_eval(preds["preds00_at_x10"], _QR_TAU_GRID, ranks_qdid) + ) + np.testing.assert_array_equal( + y0t_qdid, + np.asarray(case["qdid_y0t"], dtype=float), + err_msg=f"QDiD imputations mismatch in '{name}'", + ) + + def test_micro_end_to_end_consistent_with_stored_qte_outputs(self, fixture): + # Guards against qte's internals diverging from raw predict.rqs usage: + # composing R's STORED imputations into ATT/QTE with the module's + # quantile helpers must reproduce the stored qte::CiC()/QDiD() outputs + # (R-vs-R consistency - no solver or tie-selection freedom involved, + # so the tolerance is arithmetic-tight). + for name, case in fixture["qr_cases"].items(): + cells = self._cells(case) + y11_sorted = np.sort(cells["c11"]["y"]) + y0t_cic = np.asarray(case["cic_y0t"], dtype=float) + att_cic = float(np.mean(cells["c11"]["y"]) - np.mean(y0t_cic)) + probs = np.asarray(fixture["metadata"]["probs"], dtype=float) + qte_cic = _quantile_type1(y11_sorted, probs) - _quantile_type1(np.sort(y0t_cic), probs) + np.testing.assert_allclose(att_cic, case["cic_ate"], atol=1e-12, rtol=0) + np.testing.assert_allclose( + qte_cic, + np.asarray(case["cic_qte"], dtype=float), + atol=1e-12, + rtol=0, + err_msg=f"CiC micro end-to-end inconsistency in '{name}'", + ) + y0t_qdid = np.asarray(case["qdid_y0t"], dtype=float) + att_qdid = float(np.mean(cells["c11"]["y"]) - np.mean(y0t_qdid)) + qte_qdid = _quantile_type7(y11_sorted, probs) - _quantile_type1( + np.sort(y0t_qdid), probs + ) + np.testing.assert_allclose(att_qdid, case["qdid_ate"], atol=1e-12, rtol=0) + np.testing.assert_allclose( + qte_qdid, + np.asarray(case["qdid_qte"], dtype=float), + atol=1e-12, + rtol=0, + err_msg=f"QDiD micro end-to-end inconsistency in '{name}'", + ) + + def test_rq_fit_exactly_optimal_per_tau(self, fixture): + # Two-sided optimality criterion: at every tau, either the + # coefficients match R's (unique-vertex case, ~1e-14 observed) or the + # check losses match to relative 1e-10 (non-trivial optimal face - + # structural for qr2's binary covariate - where both solvers return + # exact but different vertices). + for name, case in fixture["qr_cases"].items(): + cells = self._cells(case) + for cell_key in ("c00", "c01", "c10"): + y = cells[cell_key]["y"] + x_design = _design_matrix(cells[cell_key]["X"]) + coef_r = np.asarray(case[f"coef{cell_key[1:]}"], dtype=float) # (k+1) x 99 + coef_py = _rq_fit(y, cells[cell_key]["X"], _QR_TAU_GRID) + assert coef_py is not None, f"LP failed in '{name}' cell {cell_key}" + for j, tau in enumerate(_QR_TAU_GRID): + if np.max(np.abs(coef_py[j] - coef_r[:, j])) < 1e-8: + continue + resid_r = y - x_design @ coef_r[:, j] + resid_py = y - x_design @ coef_py[j] + loss_r = np.sum(np.where(resid_r >= 0, tau * resid_r, (tau - 1) * resid_r)) + loss_py = np.sum(np.where(resid_py >= 0, tau * resid_py, (tau - 1) * resid_py)) + assert abs(loss_py - loss_r) <= 1e-10 * max(abs(loss_r), 1.0), ( + f"'{name}' cell {cell_key} tau={tau:.4f}: coefficients differ " + f"AND losses differ (R {loss_r!r} vs Py {loss_py!r}) - the " + "solver is suboptimal, not merely tie-breaking." + ) + + def test_lalonde_ties_warning(fixture, probs): """lalonde re78 zeros trigger the discreteness warning; points still match.""" scenario = fixture["scenarios"].get("lalonde_psid") @@ -216,6 +466,45 @@ def test_se_parity(self, fixture, probs, ci_params, block_key): f"(n_boot={n_boot})" ) + @pytest.mark.slow + @pytest.mark.parametrize("block_key", ["cic_cov_panel", "qdid_cov_rcs"]) + def test_se_parity_covariates(self, fixture, probs, ci_params, block_key): + """Covariate-path bootstrap SE parity (slow: every replicate refits + 2-3 x 99 quantile-regression LPs, so ~199 replicates take ~1-2 min + regardless of the Rust backend - hence the smaller min_n than the + documented min_n=199 convention, with the <100-replicate 0.40 + threshold absorbing the extra noise). Vertex/knot tie-selection + differences (see header) contribute variance far below these + statistical thresholds. + """ + block = fixture["se_block"].get(block_key) + if block is None: + pytest.skip(f"se_block {block_key} missing from fixture") + scenario = fixture["scenarios"][block["scenario"]] + covariates = _as_list(block["covariates"]) + method, _, mode = block_key.split("_") + df = pd.DataFrame({k: scenario["data"][k] for k in scenario["data"]}) + + n_boot = ci_params.bootstrap(199, min_n=99) + res = _fit(df, method, mode, probs, n_bootstrap=n_boot, seed=42, covariates=covariates) + + att_threshold = 0.40 if n_boot < 100 else 0.15 + r_ate_se = float(block["ate_se"]) + rel = abs(res.se - r_ate_se) / r_ate_se + assert rel < att_threshold, ( + f"{block_key}: ATT SE rel diff {rel:.3f} vs R " + f"({res.se:.5f} vs {r_ate_se:.5f}, n_boot={n_boot})" + ) + + qte_threshold = 0.40 if n_boot < 100 else 0.25 + r_qte_se = np.asarray(block["qte_se"], dtype=float) + py_qte_se = res.quantile_effects["se"].to_numpy() + rel_q = np.abs(py_qte_se - r_qte_se) / r_qte_se + assert np.nanmax(rel_q) < qte_threshold, ( + f"{block_key}: worst per-quantile SE rel diff {np.nanmax(rel_q):.3f} " + f"(n_boot={n_boot})" + ) + def test_sup_t_crit_loose_parity(self, fixture, probs, ci_params): """The sup-t critical value is a bootstrap quantile - compare loosely.""" block = fixture["se_block"].get("cic_panel") diff --git a/tests/test_doc_snippets.py b/tests/test_doc_snippets.py index e1d9e3fc..efc1e58a 100644 --- a/tests/test_doc_snippets.py +++ b/tests/test_doc_snippets.py @@ -38,6 +38,7 @@ "api/honest_did.rst", "api/pretrends.rst", "api/power.rst", + "api/changes_in_changes.rst", "python_comparison.rst", "r_comparison.rst", ] diff --git a/tests/test_methodology_changes_in_changes.py b/tests/test_methodology_changes_in_changes.py index 9d4a72f0..db759a7f 100644 --- a/tests/test_methodology_changes_in_changes.py +++ b/tests/test_methodology_changes_in_changes.py @@ -291,3 +291,152 @@ def test_interior_range_construction(): q_lower, q_upper = _interior_range(cells) assert q_lower == 0.25 assert q_upper == 0.75 + + +# ============================================================================= +# Covariate path (qte xformla parity route - Melly-Santangelo QR pipeline) +# ============================================================================= + + +class TestCovariateMethodology: + """Methodology checks for the conditional (quantile-regression) path. + + Deliberate non-test: monotone-transform equivariance does NOT extend to + the covariate branch (linear-in-covariates quantile regression is not + equivariant to nonlinear monotone transforms of the outcome), so the + unconditional equivariance test has no covariate analogue - documented in + the REGISTRY covariate Note rather than asserted here. + """ + + @staticmethod + def _make_shift_dgp(n_per_cell=299, seed=0, effect=1.0, shift=0.8, trend_slope=0.6): + """Covariate-composition confounding: x distributions differ by group + and the time trend depends on x, so unconditional CiC/QDiD are biased + while the conditional versions recover the constant effect. + + Cell size is coprime to 100 (see the parity-test header): avoids QR + vertex degeneracy so the test isn't sensitive to solver tie-breaking. + """ + rng = np.random.default_rng(seed) + frames = [] + for g in (0, 1): + for t in (0, 1): + x = rng.uniform(0, 2, n_per_cell) + shift * g + u = rng.normal(0, 0.4, n_per_cell) + # The x -> LEVEL link is deliberately weak (0.15) while the + # x -> TREND link is strong: unconditional CiC absorbs any + # time change expressible as a monotone transformation of the + # outcome, so a trend strongly correlated with the outcome + # level would be (correctly!) soaked up even without + # covariates. Decoupling trend from level makes the + # unconditional estimator genuinely biased while the + # conditional one stays valid (within x, the period change is + # an additive shift). + y = 0.5 + 0.15 * x + trend_slope * x * t + u + effect * g * t + frames.append(pd.DataFrame({"treated": g, "post": t, "x": x, "y": y})) + return pd.concat(frames, ignore_index=True) + + @pytest.mark.parametrize("cls", [ChangesInChanges, QDiD], ids=["cic", "qdid"]) + def test_irrelevant_covariate_matches_unconditional(self, cls): + # x independent of group, period, and outcome: the conditional + # estimator adds only QR estimation noise and must agree with the + # unconditional one at moderate N. + rng = np.random.default_rng(1) + n = 251 # coprime to 100 + frames = [] + for g in (0, 1): + for t in (0, 1): + y = rng.normal(0.3 * t + 0.5 * g * t, 1.0, n) + frames.append( + pd.DataFrame({"treated": g, "post": t, "x": rng.uniform(0, 1, n), "y": y}) + ) + df = pd.concat(frames, ignore_index=True) + r_cov = fit_quiet(cls(n_bootstrap=0), df, covariates=["x"]) + r_unc = fit_quiet(cls(n_bootstrap=0), df) + assert r_cov.att == pytest.approx(r_unc.att, abs=0.1) + # Loose atol: tail-quantile QR estimates are noisy at this N (the + # 0.05/0.90 grid points drive the worst deviations); a composition + # bug would err at O(0.5-1). + np.testing.assert_allclose( + r_cov.quantile_effects["qte"].to_numpy(), + r_unc.quantile_effects["qte"].to_numpy(), + atol=0.35, + ) + + @pytest.mark.parametrize("cls", [ChangesInChanges, QDiD], ids=["cic", "qdid"]) + def test_covariates_correct_compositional_confounding(self, cls): + # The Melly-Santangelo motivation (their Figures 1-2 analysis): when + # group covariate compositions differ AND trends vary with x, the + # unconditional estimator is biased; conditioning on x restores the + # constant effect. Bias magnitude ~ trend_slope * E[x_treated - x_control]. + df = self._make_shift_dgp(seed=2) + r_cov = fit_quiet(cls(n_bootstrap=0), df, covariates=["x"]) + r_unc = fit_quiet(cls(n_bootstrap=0), df) + # Measured across seeds: cov bias <= 0.14, unc bias >= 0.33. + assert abs(r_cov.att - 1.0) < 0.2 + assert abs(r_unc.att - 1.0) > 0.25 + assert abs(r_cov.att - 1.0) < abs(r_unc.att - 1.0) + + def test_constant_covariate_reduces_to_order_statistics(self): + # Hand-check via the constant-covariate reduction: with x constant, + # each per-cell QR collapses to an intercept-only fit whose fitted + # value at tau is the check-loss minimizer - the ceiling order + # statistic y_(ceil(n*tau)) (unique here because no grid tau puts + # n*tau on an integer when n = 7). From those known predictions the + # whole CiC composition is hand-derivable with the module's own + # step-function helpers, so the full fit must reproduce it. + from diff_diff.changes_in_changes import ( + _QR_TAU_GRID, + _design_matrix, + _fhat_eval, + _qhat_eval, + _rq_fit, + ) + + rng = np.random.default_rng(3) + n = 7 + cells_y = { + (0, 0): np.sort(rng.normal(0.0, 1.0, n)), + (0, 1): np.sort(rng.normal(0.5, 1.2, n)), + (1, 0): np.sort(rng.normal(0.2, 0.9, n)), + (1, 1): np.sort(rng.normal(1.1, 1.1, n)), + } + frames = [ + pd.DataFrame({"treated": g, "post": t, "x": 2.0, "y": y}) + for (g, t), y in cells_y.items() + ] + df = pd.concat(frames, ignore_index=True) + + # 1. The LP's fitted values equal the ceiling order statistics. + x_const = np.full(n, 2.0) + for y_cell in cells_y.values(): + coefs = _rq_fit(y_cell, x_const[:, None], _QR_TAU_GRID) + assert coefs is not None + fitted = _design_matrix(x_const[:1, None] * 0 + 2.0) @ coefs.T # 1 x 99 + expected = np.array([y_cell[int(np.ceil(n * tau)) - 1] for tau in _QR_TAU_GRID]) + np.testing.assert_allclose(fitted[0], expected, atol=1e-7) + + # 2. End-to-end CiC equals the hand-derived composition. + def order_stat_preds(y_cell): + row = np.array([y_cell[int(np.ceil(n * tau)) - 1] for tau in _QR_TAU_GRID]) + return np.tile(row, (n, 1)) + + y10 = cells_y[(1, 0)] + ranks = _fhat_eval(order_stat_preds(cells_y[(0, 0)]), _QR_TAU_GRID, y10) + y0t = _qhat_eval(order_stat_preds(cells_y[(0, 1)]), _QR_TAU_GRID, ranks) + expected_att = float(np.mean(cells_y[(1, 1)]) - np.mean(y0t)) + + res = fit_quiet(ChangesInChanges(n_bootstrap=0), df, covariates=["x"]) + assert res.att == pytest.approx(expected_att, abs=1e-6) + + # 3. QDiD analogue: own-cell ranks, additive imputation, and the + # asymmetric Q7/Q1 quantile-type pair ported from qte. + ranks_q = _fhat_eval(order_stat_preds(y10), _QR_TAU_GRID, y10) + y0t_q = ( + y10 + + _qhat_eval(order_stat_preds(cells_y[(0, 1)]), _QR_TAU_GRID, ranks_q) + - _qhat_eval(order_stat_preds(cells_y[(0, 0)]), _QR_TAU_GRID, ranks_q) + ) + expected_att_q = float(np.mean(cells_y[(1, 1)]) - np.mean(y0t_q)) + res_q = fit_quiet(QDiD(n_bootstrap=0), df, covariates=["x"]) + assert res_q.att == pytest.approx(expected_att_q, abs=1e-6)