[WIP] Add efficient DiD estimator (Chen, Sant'Anna & Xie 2025)#260
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marcelortizv wants to merge 93 commits into
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[WIP] Add efficient DiD estimator (Chen, Sant'Anna & Xie 2025)#260marcelortizv wants to merge 93 commits into
marcelortizv wants to merge 93 commits into
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Adds 14 new R source files implementing the Chen, Sant'Anna & Xie (2025) Efficient DiD estimator for staggered-adoption balanced panels. Key features: - PT-All and PT-Post regimes via enumerate_valid_pairs_edid() - Omega* covariance matrix and optimal inverse-covariance weights - Cluster-robust SE via EIF sandwich formula - Multiplier bootstrap (Rademacher, Mammen, Webb) - WIF correction in overall/event-study/group aggregations - edid_fit S3 class with print/summary/coef/vcov/as.data.frame methods - Covariate and survey paths are clean stubs (stop with message) - No new package dependencies (svd() replaces MASS::ginv) Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
…p field names) - BUG-1: remove spurious `eif - att_gt` subtraction in compute_eif_nocov_edid(); the score is already zero-mean by construction (each group contribution is demeaned) - BUG-2: safe_inference_edid() now returns inference_valid=FALSE (with non-NA se but NA CIs/p-value) when att is non-finite, fixing the valid=TRUE/NA-CI inconsistency - BUG-3: resolves automatically from BUG-1 fix - BUG-4: rename overall_draws→overall_b, event_study_draws→event_study_b, group_draws→group_b in run_multiplier_bootstrap_edid() and update callers in edid.R Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Appended edid() feature bullet to NEWS.md under the 2.3.1.904 development header. Produced ARCHITECTURE.md and log-entry.md in the run directory documenting the EDiD module structure, panel_obj/edid_fit schemas, (g,t) cell loop, PT-All vs PT-Post pair enumeration, Omega* construction, aggregation/bootstrap flows, and 4 bug resolutions (BUG-1 through BUG-4). Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Implements the efficient DiD estimator from Chen, Sant'Anna & Xie (2025) supporting PT-All and PT-Post parallel trends assumptions. Features (Priorities 1-6): - Data validation and balanced-panel preprocessing - Valid-pair enumeration for PT-All and PT-Post regimes - Closed-form efficient DiD via inverse-covariance weights (no-cov path) - Analytical EIF-based SEs (iid and cluster-robust) - Overall, event-study, and group aggregation with WIF correction - Multiplier bootstrap (Rademacher, Mammen, Webb) with cluster expansion Deferred to follow-up: DR covariate path, survey support, Hausman pretest. 207 new edid tests pass; 777 full-suite pass; 0 regressions. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Rename edid() args: outcome->yname, unit->idname, time->tname, first_treat->gname, alpha->alp, cluster->clustervars, n_bootstrap->bstrap+biters; control_group "never_treated"->"nevertreated" - Add G=0->Inf auto-conversion so att_gt() datasets work directly - Rewrite print.edid_fit() to MP-style ATT(g,t) table with sig codes - summary.edid_fit() delegates to print then appends overall/ES/group - Add bstrap field to edid_fit object for CI label selection - Create R/edid-aggte.R: aggte_edid(), print/summary.AGGTEobj_edid - Update compare_att_gt_edid.R: new arg names, drop G_edid column - Update all edid test files to use renamed arguments Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
…rison cohort Replaces the PT-All loop in enumerate_valid_pairs_edid() to use only treated cohorts as comparison cohorts (never-treated appears only as the time control inside each moment). Self-pairs (gp==target_g) include period_1 as a valid tpre (degenerate CS DiD); cross-pairs exclude it. This eliminates T-1 redundant gp=Inf rows, resolves near-singular Omega, and produces a correctly-specified analytical Omega for PT-All. Also removes dead gp=Inf branches in compute_omega_star_nocov_edid(), compute_generated_outcomes_nocov_edid(), and compute_eif_nocov_edid() that handled the now-impossible Inf comparison cohort case. PT-Post paths are unchanged. ATT estimates match author's reference to < 1e-10; pair count for cell (g=3, t=any) on 10-period 3-cohort data is 13 (was: 10 finite + 9 redundant Inf). Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Replace edid-covariates.R stub (stop() functions) with empty placeholder - Add xformla argument to edid(), validate_edid_inputs(), prepare_edid_panel(), fit_edid_cells() - Extract covariate_matrix in prepare_edid_panel() from xformla formula - Add xformla/covariates validation in edid-validate.R (covariates now deprecated-errors) - Dispatch to covariate EIF path (edid-cov.R + edid-cov-eif.R) when xformla is non-trivial - Fix bs_objects NULL-sentinel bug in build_basis_matrix_edid/predict_basis_edid that broke cross-fitting Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
…pass Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Untrack personal working files (PDFs, audit/spec/plan markdown notes) and move compare_att_gt_edid.R into benchmark/. Add the personal files to .gitignore so they remain on disk locally but aren't shared, and to .Rbuildignore (along with benchmark/) so they don't ship in the R package build. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
The R CMD check failed because:
- Roxygen plain-text math like (g', t_pre), [Y_s - Y_1 | G=g', X], and
r_{g,Inf} confused the Rd parser (apostrophes interpreted as quoted
strings, brackets as link targets, _{...} as markdown emphasis). Wrap
these in \eqn{} or rename g' -> gp to match the variable name in code.
- as.data.frame.edid_fit() signature didn't match the as.data.frame()
generic; add row.names/optional and move which after ... .
Also regenerate stale Rd files for the covariate-path functions.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Three bugs fixed: 1. aggte_edid(type="dynamic") and aggte_edid(type="group") reused the pre-baked "simple" overall instead of computing type-specific overalls. Now: - dynamic overall = mean(ES(e)) for e >= 0 (equal weights, no WIF) - group overall = pi_g-weighted average of per-group ATTs (with WIF) Both match the formulas in compute.aggte.R (did::aggte). 2. enumerate_valid_pairs_edid() for PT-Post excluded tpre == period_1, which caused edid(pt_assumption="post") to return NA for the earliest-treated cohort (e.g., group 8 in the Dobkin application). The standard 2x2 DiD with period_1 as base is valid. 3. EIF index tracking through balance_e/min_e/max_e/na.rm filtering in aggte_edid — original indices into es_list/gr_list are now preserved so that the correct EIFs are used for SE computation. Also stores cohort_fractions and unit_cohorts in edid_fit so that aggte_edid can compute pi_g-weighted group overalls with WIF. Verified against: - did::aggte() on the same data (formula match) - did::att_gt(base_period="varying") for PT-Post (diff ~1e-12) - Paper equations (3.8), (3.9), Theorem 3 SE formula - All 519 existing edid tests pass
Two corrections to the doubly-robust covariate path so the implementation
matches Chen, Sant'Anna & Xie (2025):
1. Cross-cohort term1 in compute_generated_outcomes_cov_edid() now uses
the full Eq. (4.4) augmentation:
(G_g / pi_g) * (Y_t - Y_1 - m_{Inf,t,t'}(X) - m_{g',t',1}(X))
replacing the partial form that subtracted only m_{Inf,t,1}(X).
The previous form was consistent but less efficient than the EIF.
2. Omega* now uses sieve-estimated conditional inverse propensities
1/p_g(X) per Eq. (3.12), rather than the unconditional 1/pi_g
approximation. A new estimate_inverse_propensity_edid() (B-spline
sieve) and estimate_all_inverse_propensities() (K-fold cross-fitting)
produce the per-unit weights threaded into compute_omega_star_cov_edid().
Verification (R=5000, n=500, 10 cores, 3 DGPs with pre/post comparison,
benchmark/edid_cov_fix_verification.R):
- Both pre- and post-fix versions are consistent (|bias| < 0.005 in all
cells across all DGPs); the term1 correction does not remove bias but
delivers the efficient EIF.
- mc_sd of point estimates drops 4-12% (DGP-dependent), matching the
expected efficiency gain from the EIF correction.
- All 519 base tests and 66 covariate tests continue to pass.
…stics The covariate path of edid() now uses plug-in (no sample-splitting) nuisance estimation, with train = test = full sample. This matches the paper's main-text proposal (Sec. 5.2, footnote 601, which explicitly warns that "sample-splitting [...] can lead to a loss of precision in DiD and ES estimation in small samples"). Why: Monte Carlo experiments at R=500, n=500 on three overlap-respecting DGPs (max 1/p_g(X) <= 16, well within Assumption O) showed that K=5 cross-fitting substantially inflates the point estimator's variance at moderate sample sizes: | DGP | mc_sd K=5 | mc_sd K=1 | reduction | edid vs att_gt | |-----|-----------|-----------|-----------|----------------| | A | 0.117 | 0.080 | -32% | K=5 22% WORSE | | B | 0.131 | 0.083 | -36% | K=5 35% WORSE | | C | 0.139 | 0.085 | -39% | K=5 42% WORSE | Under K=5 the efficient EDiD estimator is actually less efficient than the just-identified DR estimator (att_gt) -- the cross-fitting noise exceeds the efficiency gain. Under K=1 the efficient estimator beats att_gt by 13-17% as the paper's theory predicts. Implementation notes: - fit_edid_cells() hardcodes K=1L; the K>1 branch in the three estimate_all_* loops is preserved (gated by `if (K_folds == 1L)`) so cross-fitting can be re-enabled by changing one line once the finite-sample SE behavior of cross-fitted plug-in variance is better understood. - The existing analytical SE estimator (sum(eif^2)/n^2) is conservative even under K=1 (over-coverage ~0.99 at n=500); this is a separate issue tracked for future investigation. Tests: 1090 PASS, 0 FAIL. Also fixed three pre-existing test helpers that passed the obsolete `covariates = NULL` argument to fit_edid_cells() (test-edid-aggregate.R lines 10/70, test-edid-bootstrap.R line 123). Supporting evidence: benchmark/edid_cov_fix_verification.R (uncommitted working-tree script).
pmax(.,0) on the fitted ratio breaks the sieve first-order condition that zeroes the outcome-regression estimation effect; removing it keeps the estimator orthogonal and improves coverage under strong selection. Original line kept commented for easy restore.
… function) compute_eif_cov_edid used w'Ytilde - ATT; the correct first-order IF for the ratio estimator ATT_hat = E_n[w'Ytilde]/E_n[G_g] is w'Ytilde - (G_g/pi_g)*ATT. The constant centering inflated the variance by ATT^2(1/pi_g - 1) with no asymptotic shrinkage (verified: SE/mc_sd 1.33-1.54 -> 1.02-1.12 in good overlap). No-covariate path uses the correct group-demeaned IF and is unaffected.
Author-comparison and original-simulation scripts plus their generated CSVs; superseded auxiliary material, removed to keep the package source focused. Recoverable from git history if needed.
- Aggregation: cohort-share weight-influence (WIF) contribution added to the event-study, group, and calendar overalls (compute_wif_contribution_edid, aggregate_calendar_edid), so the aggregate influence functions account for the estimated cohort shares. - Inverse-propensity helpers (estimate_all_inverse_propensities, estimate_inverse_propensity_edid) supply the conditional 1/p scalings of Omega*(X) (Eq. 3.12) in place of the unconditional fallback. - vcov.edid_fit: cluster-robust branch matching the reported cluster-robust standard errors; docstring no longer claims to return bootstrap variances. - Edge guards: inverse-propensity n_gp == 0, NA weighted generated outcomes, tname finiteness, at least one finite cohort, and balanced cross-fit folds now raise informative errors instead of returning Inf/NA. - Documentation: edid() and compute_*_edid docstrings aligned with Eqs. (3.12)/(4.4) and the aggregation definitions; pointwise-weight docstring states the shrinkage + eigenvalue-floor regularization. - Tests: new test-edid-paper-faithfulness.R; extended cov-eif / integration / pairs-validation tests (580 edid tests pass, 0 failures). New man pages for the helpers above. Note: a full document() + R CMD check pass is still pending before this branch becomes a PR.
as_MP_edid() builds a did::MP object from an edid fit -- group-time estimates, influence functions, and a DIDparams -- so did::aggte() and the rest of the did ecosystem can aggregate edid output unchanged, with no edits to aggte.R / compute.aggte.R. edid() now stores the metadata the MP needs (idname, tname, gname, time_periods, all_units, panel). Verified: aggte() runs for simple/group/dynamic/calendar on the edid MP, with analytical SEs, and the dynamic event-time ATT reproduces edid's per-cell estimates. Requires store_eif = TRUE. First step toward routing edid aggregation through did::aggte instead of edid's own aggregation code. Tests: test-edid-mp.R.
aggte_edid() now builds a did::MP (as_MP_edid) and delegates to did::aggte(), returning a standard did::AGGTEobj, so edid aggregation uses the published CS2021 definitions and inherits did's print / summary / tidy methods. Verified numerically identical to the previous edid-native aggregation (att ~4e-16, se ~6e-17, 4 seeds x 4 types). edid() now always retains the influence functions (as att_gt() always returns $inffunc); 'store_eif' is kept for backward compatibility but no longer gates storage. Removed the now-dead AGGTEobj_edid print/summary methods (did's AGGTEobj methods apply) and their man pages. 589 edid tests pass.
…lete edid-aggregate.R edid() now stores its aggregations as standard did::AGGTEobj objects ($overall/$event_study/$group/ $calendar/$simple), built via aggte_edid() -> did::aggte() on the edid MP. The edid_fit methods (summary/coef/vcov/as.data.frame) read AGGTEobj fields (overall.att/se, att.egt/se.egt/egt) and the per-element influence functions in $inf.function; summary() delegates event-study/group/calendar display to did's print.AGGTEobj. So summary()/coef()/vcov() now follow did's format and definitions. Deletes edid-aggregate.R (487 lines: aggregate_*_edid + compute_wif_contribution_edid), its test, and 5 man pages -- aggregation is now entirely did::aggte-driven (verified numerically identical to the prior edid-native aggregation: att ~4e-16, se ~6e-17). Migrated the public-contract assertions in test-edid-cov-basic / integration / paper-faithfulness from the old scalar-list fields ($overall$att, ...) to the AGGTEobj fields ($overall$overall.att, ...); the underlying values are unchanged. 569 edid tests pass.
…GTEobj aggregations Regenerate the man pages with roxygen2 8.0.0 (new package baseline; RoxygenNote bumped). Update edid()'s @return to describe the did::AGGTEobj aggregation slots ($overall / $simple / $event_study / $group / $calendar) and the always-stored $eif. Remove the now-vestigial store_eif argument: the influence functions are always retained (as att_gt() always returns $inffunc), since the aggregations are built from them. Dropped store_eif from edid()'s signature/@param, pass store_eif = TRUE internally to fit_edid_cells(), updated as_MP_edid() docs, and removed store_eif usages from the tests (the moot "requires store_eif" test deleted). 568 edid tests pass.
…na 2021, Remark 10) The clustered multiplier bootstrap now aggregates the influence function to cluster SUMS rather than cluster means: cluster_sum_if = rowsum(inf.func, cluster); bres = sqrt(n_clusters) * mb(cluster_sum_if); se = bSigma * sqrt(n_clusters) / n. For equal-sized clusters this matches the previous cluster-mean aggregation; for unbalanced clusters and repeated cross-sections it is the correct cluster-sum form. The no-clustering path is unchanged (n_clusters = n reduces se to bSigma / sqrt(n)). Aligns this fork with the fix merged in bcallaway11/did PR bcallaway11#261. 338 cluster/bootstrap/aggte tests pass.
… edid-bootstrap.R Under bstrap = TRUE, the cell-level SEs and simultaneous critical value now come from did::mboot on the influence-function matrix, and the aggregations bootstrap through aggte_edid() -> did::aggte(bstrap = TRUE), so $att_gt, $overall, $event_study, ... all carry multiplier-bootstrap inference consistent with att_gt. Verified: bootstrap SEs match the analytical SEs up to Monte-Carlo error (linear influence function) for both clustered and unclustered designs. Deletes edid-bootstrap.R (run_multiplier_bootstrap_edid / generate_multiplier_weights_edid / compute_bootstrap_stats_edid), its test, and 3 man pages. Removes the bootstrap_weights argument (did's mboot uses BMisc's multiplier; att_gt-consistent). Builds on the mboot cluster-sum fix so edid's clustered bootstrap uses the correct cluster-sum form. 500 edid tests pass.
Fold edid-linalg.R (pseudoinverse / condition-number / weighted-OLS helpers) and the edid-imports.R @importFrom block into edid-utils.R, and delete the intentionally-empty edid-covariates.R placeholder. No behavior change (the merged helpers are unexported internals; NAMESPACE imports unchanged). Reduces the edid file count 16 -> 13. 500 edid tests pass.
- Qualify stats::.lm.fit in solve_ols_edid (no visible global function NOTE). - Replace raw braces with parens in roxygen titles/text (compute_pointwise_weights_edid, estimate_all/inverse_propensity_edid) -- fixes the checkRd 'Lost braces' NOTEs. - Add CLAUDE.md to .Rbuildignore (non-standard top-level file NOTE). R CMD check: 0 errors, 0 warnings, 0 NOTEs.
Collaborator
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Pushed a consolidation pass that makes Compatibility
Bootstrap
Cleanup
Status: |
…efficient-DiD-estimator Brings the branch up to date with master (8 commits, including the merged PR bcallaway11#261 cluster-robust inference). Resolves the only conflict, R/mboot.R, by taking master's version -- the reviewed PR bcallaway11#261 Remark-10 cluster-sum implementation supersedes the local port (0867d6c), which made the same change. Regenerated docs with roxygen2 8.0.0. The edid consolidation routes aggregation/bootstrap through master's aggte/mboot/getSE; verified against the merged infrastructure: edid suite 500 pass, full did suite 1135 pass, 0 failures.
The kernel routine emitted a warning() whenever n > 1000, which is an ordinary sample size for DiD -- so it fired on essentially every covariate-path call (42 of 43 test-suite warnings). It signals nothing about result correctness, only that the O(n^2) kernel may be slow at large n. Demote it to an informational message(), emitted only in interactive sessions for n > 5000 and silenceable via options(edid.quiet = TRUE); the inner conditional-covariance step was already optimized to matrix-vector form, so the n x n cost only bites in the tens of thousands. Also mark the small-n nonlinear-DGP covariate test, which deliberately stresses overlap, as expecting the extreme-propensity diagnostic. edid suite: 500 pass, 0 warnings.
This reverts commit 46eba84.
…ridge + weightsname/toolkit hardening Four audited rounds re-establishing full option-matrix compatibility (no shortcuts): - Effective-n (Kish) ridge: regularize the weighted Omega* by lambda = H / n_eff with n_eff = (sum w)^2 / sum w^2 -- raw n under-regularizes the weighted Omega* by n/n_eff. Byte-identical on the unweighted path (dispersion = 0). (R/edid-nocov.R) - Over-id eigen-ridge fix: lift the small eigenvalues of the IF-difference quadratic form to a dispersion-scaled floor, fixing spurious over-id rejection under dispersed weights / thin cohorts. (R/edid-hausman.R) - Covariate ridge path (edid-cov-eif / kernfast / sieve): Omega(X) regularization, asymptotically negligible (no-cov == cov-at-trivial-X invariant preserved). - weightsname + input-validation hardening; frontier (Andrews-Chen-Tecchio) toolkit. Audit battery: full testthat suite 3394 pass / 0 fail (67 expected thin-overlap + group-aggregation warnings, 55 skips). New tests: cov-ridge, weightsname, overall-consistency. NEWS + roxygen updated in the same round. Note: 3 large regenerable testthat-dump RDS under quality_reports/.../overid/ were deliberately left untracked (repo-bloat; not source).
…ame x xformla)
Enable a correct, fully-audited observation-weighted covariate path. Weights now
propagate through every option:
- Weighted nuisance WLS (propensity ratios, inverse propensities, conditional means,
exp-link Riesz) + the three estimate_all_* aggregators.
- Weighted Omega*(X) on all three smoothers (kernel / kernel_orig / sieve): weighted
Nadaraya-Watson / WLS conditional moments + weighted pooling; obs-weighted pi_inf.
- Obs-weighted (Hajek) plug-in moment / EIF; obs-weighted overlap treated-mass m_common
(was unweighted -> renorm != 1 under weights); obs-weighted ACH estimation-effect.
- Weight-estimation (Sigma_Omega) channel: Hajek marginal weight in psi_Omega + the
inv-p coupled_C (kernel + sieve), gmm sample-cov weight + its estimation-effect
correction, and the higher_order cell-Hessian.
- Clustering and the cohort-share aggregations inherit weighting through the EIF / .w.
- Relax the weightsname x xformla guard.
Validated: weighted ACH FD-oracle to 1e-5; weight-channel + cell-Hessian FD-oracles
(analytic == finite-difference under dispersed weights); MC nominal coverage (kernel
and sieve weighted, unbiased); a CONSTANT weight column reproduces the unweighted fit
byte-identically across the full option matrix -- smoother x ratio_method x weight_scheme
{efficient,averaged,gmm,uniform} x {plug-in,estimation_effect,misspec_robust,higher_order}
x omega_cov_shrink x bs_df="ic" x PT-Post x trimming x {group,event_study,calendar,overall}
x clustering x multiplier bootstrap x the hausman/sargan/frontier/adaptive/weights toolkit
(test-edid-weighted-cov-options.R, 88/0). Full testthat 3556/0; unweighted path
byte-identical (weightsname = NULL; invariant harness 0 diff).
New tests: test-edid-weighted-cov-{wls,omega,e2e,toolkit,gmm,higher-order,options}.R.
Audit trail + FD/MC scripts: quality_reports/wcov/.
…ull weighted-covariate edid support (weightsname x xformla) + prior weightsname/toolkit hardening
…ters (document() the new weights/obsw args)
…id 2.5.0) Brings the PR up to date with master (did 2.5.0): GPL-2 -> GPL-3 license, BMisc deprecation fixes (rhs_vars / get_list_element), CI/pkgdown/docs. Mechanical conflict resolution only (DESCRIPTION, imports.R, .gitignore, .Rbuildignore, one upstream test); the edid algorithm code auto-merged. Verified: package loads + full testthat 3554/0 on the merged state.
…p dead README library() calls R-CMD-check (all platforms) errored in test-edid-weighted-cov-omega.R: its make_omega_panel coded never-treated as g=0, but tests that call prepare_edid_panel / the Omega builders DIRECTLY must use the internal never-treated convention (Inf). edid() recodes a user-facing g=0 to Inf at its boundary -- so production was always correct -- but the direct-call unit test bypassed that, making 0 a phantom treatment group with no never-treated comparison (pi_inf=0 -> 1/pi_inf=Inf -> Inf*0=NaN -> eigen crash). Code never-treated as Inf and document the convention so it cannot regress. All three Omega builders (kernel/kernel_orig/sieve) x both branches now pass: finite, w==1 byte-identical to unweighted, dispersed weights move Omega. Full testthat suite green (3560/0/0). README.Rmd: remove dead library(ggpubr) and library(gridExtra) -- never called and undeclared in DESCRIPTION -- so the upstream build-check workflow's devtools::build_readme() step succeeds without adding any dependency.
… build CI FEATURE -- no never-treated group. When every unit is eventually treated (no gname == Inf and no gname == 0), edid() now follows the att_gt() control_group = "nevertreated" convention instead of erroring: it drops every period at/after the last cohort's effective onset (g_max - anticipation) and recasts that cohort as never-treated, so it anchors the comparison over the retained pre-onset window (with a warning). Implemented as a shared internal helper (.edid_coerce_no_never_treated) at the edid() boundary; the SAME helper is reused by edid_perturbation_bootstrap()'s inline panel rebuild so both produce an identical panel. Guards error on a single treated cohort, or when fewer than two pre-onset periods remain. Validated byte-identical (AUTO==MANUAL) to running edid() on the hand-transformed panel across the no-cov / covariate paths, all weight schemes, observation weights, every aggregation, clustering, and all three bootstraps (multiplier, refit, perturbation). New tests in test-edid-no-never-treated.R. FIX (R CMD check) -- thin-cohort fingerprint fragility. The legacy byte-identity fingerprints in test-edid-thin-cohort.R were compared with expect_identical, which fails under R CMD check's byte-compiled / installed package (and cross- platform BLAS) even though load_all reproduces them exactly. Relax to a tight tolerance under covr AND R CMD check; keep exact identity in interactive dev. FIX (build-check) -- declare badger in Suggests so devtools::build_readme() (the README badge chunk) succeeds. Dead library(ggpubr)/library(gridExtra) were already removed. Also: gate the no-never-treated coercion on !anyNA(gname) so NA cohorts defer to validation (no spurious warning / base max() warning); make the per-cell is_pre flag anticipation-aware (t < g - anticipation) for the post-cell diagnostics (reported att/se and aggregations unaffected). Full testthat suite: 3609 pass / 0 fail / 0 error.
…ger dep Declaring badger in Suggests (prev commit) let build-check install it, but badger's badge_*() functions query git/GitHub at render time (git_default_branch_(github_remote_config())), which fails in build-check's CI sandbox. Replace the dynamic badger chunk in README.Rmd with the equivalent STATIC badge markdown (identical rendered output, no render-time dependency) and drop badger from Suggests (no longer used anywhere). Verified: README.Rmd renders cleanly end to end.
Copilot AI
added a commit
that referenced
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Jun 17, 2026
The build-check CI job was failing because PR #260 adds 16 new exported edid functions (edid, aggte_edid, as_MP_edid, and S3 methods/tools) that were missing from the _pkgdown.yml reference index. pkgdown requires all exported topics to be listed in _pkgdown.yml or marked @Keywords internal. Added them in three new sections: - DiD Methods: edid, aggte_edid, as_MP_edid (alongside att_gt/aggte) - Efficient DiD methods: print/summary/coef/vcov/as.data.frame + weights - Efficient DiD specification testing: hausman/sargan/adaptive/frontier/bootstrap
…heck pkgdown fix)
pkgdown::build_site() (the build-check job) requires every exported, documented
topic to appear in the reference index; PR260's 16 new edid exports were missing,
so build_reference_index() failed ("Reference metadata not ok ... 16 topics
missing from index"). Add them in three sections (equivalent to the independent
Copilot fix in PR bcallaway11#265): edid / aggte_edid / as_MP_edid alongside att_gt / aggte;
an "Efficient DiD: Plotting, Summarizing, and Methods" section (print/summary/
coef/vcov/as.data.frame methods + edid_weights + edid_weight_plot); and an
"Efficient DiD: Specification Testing and Robustness" section (hausman / sargan /
adaptive / frontier / perturbation_bootstrap / refit_bootstrap).
…nization, efficient plug-in toolkit Over-identification / inference overhaul for the efficient DiD estimator (see NEWS.md for full detail). Over-id / Hausman test: finite-sample AHT effective-df F reference (m = G_eff - 1) replacing the chi-square (m -> inf) corner; fixes the few-cluster / dispersed-weight over-rejection. The dispersed-weight eigen-ridge is removed (the F is the sole finite-sample correction). One change in the shared chokepoint .edid_if_diff_quadform, inherited by edid_hausman/sargan/frontier/adaptive; surfaces m_eff / df2 / m_sat (fragility flag). -> chi-square as G_eff -> inf (no-op for balanced i.i.d. large n). Omega-EE harmonization: the first-order misspecification weight-estimation influence function is now ON by default for any non-uniform weight_scheme on BOTH paths. Covariate: psi_Omega(X). No-covariate (new): psi_omega = D %*% mbar, the IF of the weighted pseudo-estimand theta_w, folded into the EIF; it is zero under correct spec (optimal-weight FOC, D %*% 1 = 0) and restores coverage of theta_w under misspecification. It composes with the existing second-order var_add (Bessel + optimization-optimism), which is also default-on for non-uniform no-covariate fits; the var_add cross-cell increment is built from the pure pre-psi EIF. FD-oracled to ~1e-9; Monte-Carlo nominal under correct spec (no double-counting) and a fix under misspecification (mean-SE/MC-SD 0.90 -> 1.00), incl. the aggregate and dispersed weights. vcov.edid_fit(): carries the combined second-order increment (sigma_quad + sigma_nocov_ee) on the att_gt AND aggregation covariances, so sqrt(diag(vcov())) equals the reported SE on every path. Over-identification toolkit uses the EFFICIENT plug-in influence function: edid_hausman / edid_sargan / edid_frontier / edid_adaptive refit the legs in the plug-in configuration (all estimation-effect channels off), so the over-id object lives on the efficient inverse-variance variance (Andrews, Chen & Tecchio 2025, Sec 5), invariant to the fits' SE convention. edid_sargan's 'inference' (match_fit/plugin_fast) argument removed; edid_hausman/frontier/adaptive gain a 'data' argument (recovered from the call, with a guard that errors on a data mismatch). Validation: full testthat 693 blocks / 0 failures (NOT_CRAN); option-matrix smoke clean; roxygen + NEWS updated; man/ regenerated.
The over-identification toolkit (edid_hausman / edid_sargan / edid_frontier /
edid_adaptive) puts each leg in the efficient plug-in configuration via
.edid_plugin_refit(), which is a pure function of (fit, data). One over-id
operation previously refit the SAME legs in edid_hausman(event_study), again in
edid_hausman(overall), again in edid_sargan, and once per window-grow step; on a
large covariate panel each refit re-estimates the full Omega*(X) nuisance.
Memoize the plug-in refit (session cache keyed by an att-vector + refit-args
fingerprint; bounded), so each unique leg is fit exactly once and window-grow
steps add no refits. Bit-identical to recomputing (the data-reproduction guard
runs on the cache miss); validated identical() across {no-cov,cov}x{unw,wt} for
every Hausman statistic/df/p, the Sargan table, and the full window-grow ladder
with options(edid_plugin_cache=FALSE) vs TRUE. ~2.3-2.5x on a moderate op; more
when the refit dominates. New edid_clear_plugin_cache(); full testthat 4212/0.
…CT "report the J") Model-level over-identification statistic following Andrews, Chen & Tecchio (2025): refit every admissible elementary comparison pair as a just-identified estimator, contrast them within each cell, and stack into the rank-aware IF-difference quadratic form (.edid_if_diff_quadform). The DiD over-identification is intrinsically LOW-RANK, so the reference df is the rank of the contrast covariance, not the nominal Q-p (per-cell / per-horizon / overall coincide up to the cells in scope). Engine (.edid_if_diff_quadform, shared with edid_hausman/edid_sargan): - Effective-rank relative floor rel_tol = "auto" = r_bare/n_eff calibrates the covariate decaying spectrum; a true no-op on the exact-zero no-covariate spectrum. Division of labor with the AHT effective-df F: the floor sets the rank (spectral scale n_eff), the F sets the few-cluster reference (m = G_eff - 1). Gated on auto/rel_tol>0, so edid_hausman/edid_sargan stay byte-identical. - Cluster-rank saturation guard: when the bare numerical rank reaches the cluster ceiling (r_bare = G_eff - 1) the joint is not estimable and is reported as NA (rank_deficient), routing to the per-cell breakdown ($cells)/edid_sargan. Fires only under coarse clustering with a large over-id, not under the default unit-level clustering. edid_overid() returns the scoped J table (overall/event_study/att_gt), the per-cell breakdown, and $rank_deficient. edid_frontier() now reports a dual frontier: the directed Hausman radius and the full-J worst-case radius (ACT Prop 5.2), with a fragile flag. Validation: full testthat 4239/0 (existing baseline byte-identical for edid_hausman/edid_sargan/edid_adaptive + new edid_overid structural / regression / cluster-rank-saturation tests). MC size + power on i.i.d./AR(1)/clustered no-covariate, covariate (effective-rank floor), weighted, and genuinely clustered designs. Real data: mpdta (no-cov df 5; +lpop df 6 from bare-cut 15) and Bailey-Goodman-Bacon (county/unit clustering per the original paper: J = 46.3, df 15, p ~ 5.6e-5; over-id rejects). New files: R/edid-overid.R, tests/testthat/test-edid-overid.R, man/edid_overid.Rd.
…luence function) Under clustering the efficient weights are now formed from the cluster moment covariance Sig_cl = crossprod(rowsum(psi, cluster))/n^2 (the covariance of cluster sums), not the independent-unit covariance Omega*, aligning the weight metric with the cluster-robust SE. This is the efficient influence function within the generated-moment model under cluster sampling: it restores the efficient <= just-identified inequality that independent-unit weights violate, and is byte-identical when there is no clustering or one unit per cluster. - No-covariate PT-All: invert Sig_cl for the weights; few-cluster/rank guard (fall back to Omega* when H >= number of active clusters); ridge intensity on the cluster effective count (not unit n). - Cluster weight-estimation corrections re-derived through Sig_cl: first-order misspecification IF (per-unit, cluster-broadcast EIF) and the second-order estimation_effect var_add (per-cluster; (G/(G-1))*2Q, no separate Bessel) plus the cross-cell increment. FD-oracled; MC-calibrated (restores coverage in the few-cluster regime). - Covariate averaged/gmm: invert the cluster covariance of the generated-outcome moments (the two coincide under clustering); first-order misspecification IF folds into the EIF. - Covariate pointwise (efficient): unchanged; conditionally (not cluster) efficient under clustering with an honest cluster-robust SE. The cluster-efficient covariate influence function (per-cluster block with the within-cluster cross-unit moment covariance) is a separate planned addition. - Rewrote the "clustering changes only SEs" regression to the new invariant (efficient point is clustering-dependent; PT-Post and uniform are not). Full test suite passes; option-matrix smoke sweep clean; the over-identification toolkit (edid_overid/edid_sargan) is byte-identical (just-identified elementary moments).
…ght recovery) The overall ES_avg / vcov(which="overall") second-order weight-estimation increment (var_add) was silently dropped for single-cohort designs: the least-squares recovery of the overall weights from the event-study influence columns degenerates when the egt block is collinear (a single cohort with a long pre-window), so the increment was skipped and the reported ES_avg SE was too small. Fix: recover the overall-aggregation weights from the known design map (.edid_overall_att_map, finite-differencing the overall estimand) as a fallback when the exact LS recovery fails; route vcov(which="overall") through the same path so the headline SE and vcov stay in parity. The genuine group/calendar skip (estimated cohort-share weights outside the egt span) is retained. The hybrid recovery (exact LS where the egt is full-rank, known-weights fallback only where LS fails) preserves byte-identity for staggered designs and corrects the single-cohort case. Audit: dobkin staggered overall SE byte-identical (absDelta=0); vcov<->headline parity <=3.5e-18; full testthat 0 failures / 3667 pass; 20-row option-matrix smoke clean; regression test added. Only caochen's ES_avg SE moves (+3.46%, 0.0167592 -> 0.0173382), bootstrap-corroborated.
…); cluster ESS for LW shrink Two clustered no-cov fixes (both latent: 0 current apps trigger them). B1: when the per-cell moment count H >= G_act (active clusters), the clustered moment covariance Sig_cl is rank-deficient and the prior code fell back to an IID (non-clustered) Sig, which lets the efficient weights exploit Sig_cl's noise null space and report a sub-floor SE (illusory precision -> a spurious ARE<1 violating the efficiency bound). Replace the IID fallback with a per-cell eigen-floor on Sig_cl (the Andrews-1987 noise-floor already used by the Hausman eigen-ridge): lift the noise null space to max_eig * max(sqrt(eps), sqrt(disp_cl/cl_n_eff)), disp_cl = max(0, H/cl_n_eff - 1), gated on H >= G_act. The efficient clustered SE then collapses to the honest equal-weight read (SE/uniform-floor 0.12 -> 1.00 on a rank-1 fixture), consistent with the rank-deficient illusory-precision regime. LW: the Ledoit-Wolf shrinkage intensity used the unit Kish ESS even when shrinking the cluster metric Sig_cl; switch to the cluster ESS (cl_n_eff = G_act) when cl_metric_on, mirroring the ridge. Default (cl_metric_on = FALSE) reproduces the legacy intensity exactly. Audit: all 5 invariant fingerprints byte-identical (ridge block/staggered/post, LW unit-metric no-cluster, none block); the H<G_act and no-cluster paths unchanged; the 4 real clustered apps (tva, kresch, xu, axbarddeng) byte-identical (B1 does not fire, LW not their config) -> no reported number moves. Full testthat 0 failures; 36-row option-matrix smoke (incl few-cluster H>=G_act) clean; regression test added (12 assertions).
…iD-estimator # Conflicts: # NEWS.md # README.Rmd
…c & pkgdown CI Three no-cov helpers gained args (cluster_indices, unit_weights, cl_metric_on, cl_n_eff) without re-running document(); the stale \usage tripped the codoc check under error_on=warning. Add the two missing @param tags, regenerate the Rd, and add edid_overid to the _pkgdown.yml reference index. Docs-only.
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Summary
This PR adds
edid(), a semiparametric-efficient difference-in-differences / event-studyestimator implementing Chen, Sant'Anna & Xie (2025), alongside the existing
att_gt(). Ittargets the same group-time
ATT(g,t)and event-study objects asatt_gt(), but uses theefficient influence function and optimal (inverse-covariance) weighting to attain the
semiparametric efficiency bound under (conditional) parallel trends.
Status: WIP. The estimator, inference, aggregation, and test suite are in place and CI is
green across all platforms. The covariate path has also had a performance + numerical-stability
hardening pass (see Performance). Remaining work (a vignette and the paper's §5
specification-testing / adaptive / robustness-frontier tools) is listed under Not yet included.
What's implemented
Entry point
edid()follows the same naming conventions asatt_gt():Estimation
PT-All (all valid
(g', t_pre)pairs) or PT-Post (a single 2×2 moment per cell).propensity ratios and conditional means, with K-fold cross-fitting.
weight_scheme) —efficient(pointwise optimalΩ*(X)⁻¹1 / 1'Ω*(X)⁻¹1, attains the bound),averaged,gmm,uniform. The constant-weightalternatives remain consistent for
ATT(g,t)but do not attain the bound.Inference
cluster-robust).
cband_method = "analytic"):the Montiel Olea & Plagborg-Møller (2019) critical value computed directly from the analytic
cluster-robust covariance — uniform bands with no bootstrap, at every level (cells, event
study, group, calendar).
cband = FALSEgives pointwise intervals.cband_method = "multiplier"; Rademacher / Mammen / Webb) as thelegacy path.
misspec_robust = TRUE). With covariatesthis folds the applicable estimation-effect corrections into the reported SE — the first-step
nuisance (ACH) correction, the weight-estimation channel, and (under the analytic band) the
higher-order ("Wick") variance term. The bundle is inert without covariates or with
weight_scheme = "uniform"; an explicitly-set fine-grained flag overrides it, andmisspec_robust = FALSEreverts to the plug-in SE. The fine-grained switches remain availableindividually (all asymptotically negligible under correct specification):
estimation_effect— first-step nuisance-estimation correction (Ackerberg, Chen & Hahn 2012).higher_order— degenerate second-order ("Wick") U-statistic variance term (analytic pathonly; requires covariates).
misspec_robust— master switch for the weight-estimation channel and the bundle above.trim_level(default200) sets the overlap-trimming threshold on the covariate path.Aggregation
aggregate(one or more ofoverall,event_study,group,calendar) builds adid::MPfrom the edid estimates and their influence functions (
as_MP_edid()) and delegates todid::aggte()(aggte_edid()). Aggregations are therefore standarddid::AGGTEobjobjects andinherit did's
print/summary/tidy/glance/ggdidmethods.balance_e/min_e/max_eare supported for the dynamic (event-study) aggregation.Methods & exports
edid(),aggte_edid(),as_MP_edid().edid_fitS3 methods:print,summary,coef,vcov,as.data.frame.Performance
The covariate (kernel) path has had a speed + numerical-stability hardening pass. Point
estimates are byte-identical, the reported SEs are equal or more robust, and every commit is
CI-green (
R CMD check --as-cranon 5 platforms + the dedicated test workflow).Direct wall-clock benchmark of the default robust path (
efficient+misspec_robust = TRUE),n=3000, T=8, G=4, 2 covariates (median of interleaved repeats):
Two stages got there: an analytic-Hessian / block-sparse-variance / caching overhaul (≈ 12×; the
finite-difference higher-order Hessian was the bottleneck), then a kernel/ψ/ACH speedup pass
(≈ 3.4×) — an 8–13× BLAS kernel build, a canonical Cholesky sup-t root, ψ conditional-mean
caching, an exact analytic ACH correction replacing per-coefficient finite differences, a shared
per-cell kernel-slice cache, and a cell-invariant
m_effhoist. The only intended numeric changevs the first implementation is the stabilized trimmed higher-order SE (median 0.3%, ≤ 5.3%);
attand the first-order EIF are byte-identical. The ≈ 40× factor is config-specific (the
finite-difference terms that dominated the baseline scale super-linearly); per-commit detail is in
the performance comment below.
Recent API cleanup (breaking, relative to earlier commits on this branch)
weights→weight_scheme(avoids colliding with did's sampling-weights convention, e.g.att_gt()'sweightsname).control_groupremoved —edid()always uses the never-treated comparison group; thenot-yet-treated path is no longer offered.
balance_eis now honored (it was previously accepted but silently ignored).aggregate(e.g.c("group", "calendar")) no longer errors.aggte_edid()gains aseedargument; the multiplier-bootstrap aggregation is now reproducible.misspec_robustnow defaultsTRUE— with covariates, the reported SE ismisspecification-robust out of the box; set
misspec_robust = FALSEfor the plug-in SE.See
NEWS.mdfor the full list.Tests
tests/testthat/test-edid-*.R(20 files, ~690 assertions): no-covariate and covariateestimation; the reported-SE-equals-EIF-plug-in identity across all weight schemes; analytic
vs multiplier bands; cluster-robust SEs;
estimation_effect/higher_order/misspec_robust;pair enumeration and input validation; aggregation and the MP bridge; paper-faithfulness checks;
and degenerate-data guards (single cluster, single-unit cohort). Tests run in a dedicated
workflow (
tests/is.Rbuildignored, soR CMD checkexcludes them); both R-CMD-check (5platforms) and the test workflow are green.
Not yet included (planned)
moment-selection procedure, the AKS adaptive estimator, and the reported-parameter robustness
frontiers.
?edidhas runnable examples; no vignette yet).edid-*.Rmodules.
Notes for reviewers
covariatesis deprecated and errors with a redirect toxformla.aggte,mboot,MP/AGGTEobj) for fullatt_gt()-compatibility of the aggregation outputs.