igerber · igerber · May 26, 2026 · May 26, 2026 · May 26, 2026 · May 26, 2026
diff --git a/CHANGELOG.md b/CHANGELOG.md
@@ -5,6 +5,11 @@ All notable changes to this project will be documented in this file.
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/),
 and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
+## [Unreleased]
+
+### Added / Changed
+- **EfficientDiD `vcov_type` threading + Results metadata harmonization (Phase 1b interstitial #4, permanently narrow).** `EfficientDiD(vcov_type=...)` now accepts `{"hc1"}` only (default). Analytical-sandwich families `{classical, hc2, hc2_bm}` and `conley` are REJECTED at `__init__` / `set_params` with methodology-rooted messages — EfficientDiD uses influence-function-based variance per Chen-Sant'Anna-Xie (2025) achieving the semiparametric efficiency bound; the per-unit EIF aggregation has no single design matrix on which hat-matrix leverage or Bell-McCaffrey Satterthwaite DOF can be defined. `cluster=` (Liang-Zeger CR1 on cluster-aggregated EIF) and `survey_design=` (TSL on combined IF) paths are unchanged. **BC break on `EfficientDiDResults`:** the `cluster` field renamed to `cluster_name`; new `n_clusters` + `vcov_type` fields added; `to_dict()` method added (mirrors TripleDifferenceResults). `DiagnosticReport._pt_hausman` updated to read the renamed `cluster_name` field for the Hausman pretest replay (`diff_diff/diagnostic_report.py:2444`). `EfficientDiD.set_params(vcov_type=bad)` raises immediately rather than deferring to `fit()` — intentional eager-validation pattern matching EfficientDiD's existing handling of `pt_assumption`/`control_group` etc, diverging from `ImputationDiD`/`TripleDifference`/`CallawaySantAnna` (which use sklearn mutate-then-validate-at-use). Survey-PSU bootstrap path returns NaN SE when fewer than 2 independent PSUs are available (was ≈0 SE from BLAS roundoff). New summary block: `Variance estimator: <label>` line rendered after the survey block when not under bootstrap; suppressed under bootstrap (replaced with `Inference method: bootstrap` + `Bootstrap replications: <n>`). Default `cluster=None` (no survey) renders "HC1 heteroskedasticity-robust" — methodologically correct because the per-unit EIF SE `sqrt(mean(EIF²)/n)` is HC1-style (no Liang-Zeger G/(G-1) finite-sample correction); diverges from `ImputationDiD` which auto-clusters at unit per BJS Theorem 3.
+
 ## [3.4.2] - 2026-05-25
 
 ### Fixed

diff --git a/TODO.md b/TODO.md
@@ -104,14 +104,15 @@ Deferred items from PR reviews that were not addressed before merge.
 | PreTrendsPower: CS/SA `anticipation=1` R-parity fixture. The PR-C R-parity goldens cover NIS power + γ_p MDV at `atol=1e-4` on four shifted-grid / regular / irregular / K=1 fixtures, but R `pretrends` has no anticipation parameter so the Python-side `_extract_pre_period_params` anticipation filter (`if t < _pre_cutoff` in `pretrends.py` lines 1138-1150 for CS; mirror in SA branch) is not R-parity-locked. Build a synthetic `CallawaySantAnnaResults` (or `SunAbrahamResults`) with `anticipation=1` and a t=-1 event-study entry that should be filtered before reaching `_compute_power_nis`, then assert the resulting γ_p matches R's `slope_for_power()` on the K=4 shifted-grid fixture. Existing PR-B MC-based tests (`TestPretrendsPropositions`) and full-VCV tests (`TestPretrendsCovarianceSource`) already cover the filter mechanically; this would close the loop against R. | `tests/test_methodology_pretrends.py::TestPretrendsParityR`, `benchmarks/R/generate_pretrends_golden.R` | PR-C follow-up | Low |
 
 
-| Thread `vcov_type` (classical / hc1 / hc2 / hc2_bm) through the standalone estimators that expose `cluster=` but not yet `vcov_type=`: `TwoStageDiD`, `EfficientDiD`. Phase 1a added the chain to DiD/MPD/TWFE; Phase 1b PR 1/8 added `SunAbraham`; PR 2/8 added `StackedDiD`; PR 3/8 added `WooldridgeDiD` OLS path. **Three interstitial PRs (post-PR-3/8) addressed the IF-based estimators separately, each permanently narrow to `{"hc1"}`**: (a) `CallawaySantAnna` per Callaway & Sant'Anna (2021) Theorem 2 (also fixed CS's bare-`cluster=` silent no-op); (b) `TripleDifference` per Ortiz-Villavicencio & Sant'Anna (2025) on the 3-pairwise-DiD decomposition; (c) `ImputationDiD` per Borusyak-Jaravel-Spiess (2024) Theorem 3 on per-unit IF aggregation (also added defensive `n_clusters<2`/`n_psu<2` NaN guard on the bootstrap path + `cluster=` + replicate-weights `NotImplementedError`). Analytical-sandwich families don't compose with IF-based variance for any of the three. This row tracks the remaining 2 (`EfficientDiD` is also IF-based and will likely adopt the same narrow contract; `TwoStageDiD` is sandwich-class). | multiple | Phase 1b | Medium |
+| Thread `vcov_type` (classical / hc1 / hc2 / hc2_bm) through the standalone estimators that expose `cluster=` but not yet `vcov_type=`: `TwoStageDiD`. Phase 1a added the chain to DiD/MPD/TWFE; Phase 1b PR 1/8 added `SunAbraham`; PR 2/8 added `StackedDiD`; PR 3/8 added `WooldridgeDiD` OLS path. **Four interstitial PRs (post-PR-3/8) addressed the IF-based estimators separately, each permanently narrow to `{"hc1"}`**: (a) `CallawaySantAnna` per Callaway & Sant'Anna (2021) Theorem 2 (also fixed CS's bare-`cluster=` silent no-op); (b) `TripleDifference` per Ortiz-Villavicencio & Sant'Anna (2025) on the 3-pairwise-DiD decomposition; (c) `ImputationDiD` per Borusyak-Jaravel-Spiess (2024) Theorem 3 on per-unit IF aggregation (also added defensive `n_clusters<2`/`n_psu<2` NaN guard on the bootstrap path + `cluster=` + replicate-weights `NotImplementedError`); (d) `EfficientDiD` per Chen-Sant'Anna-Xie (2025) EIF aggregation achieving the semiparametric efficiency bound (also renamed `EfficientDiDResults.cluster` → `cluster_name`, added `n_clusters`/`vcov_type` fields + `to_dict()`, added defensive survey-PSU n<2 NaN guard, eager set_params validation diverging from sibling IF-based estimators). Analytical-sandwich families don't compose with IF-based variance for any of the four. This row tracks the remaining 1 (`TwoStageDiD` is sandwich-class with GMM-corrected meat). | `diff_diff/two_stage.py` | Phase 1b | Medium |
 | Extend `SunAbraham` with `vcov_type="conley"` (Conley spatial-HAC) as a first-class feature: thread `conley_coords` / `conley_cutoff_km` / `conley_metric` / `conley_kernel` / `conley_time` / `conley_unit` / `conley_lag_cutoff` through `_fit_saturated_regression`. Phase 1b PR 1/8 deferred this; SA currently rejects `vcov_type="conley"` at `__init__` with a deferral message. | `diff_diff/sun_abraham.py` | follow-up | Medium |
 | Extend `StackedDiD` with `vcov_type="conley"` (Conley spatial-HAC) — thread the six `conley_*` params through `solve_ols` at `stacked_did.py:419` (and the `_refit_stacked` closure at `:444`). Phase 1b PR 2/8 deferred this; StackedDiD currently rejects `vcov_type="conley"` at `__init__` with a deferral message. Same shape as the SunAbraham conley follow-up. | `diff_diff/stacked_did.py` | follow-up | Medium |
 | Extend `WooldridgeDiD` with `vcov_type="conley"` — thread the six `conley_*` params through `solve_ols` in `_fit_ols`. Phase 1b PR 3/8 deferred this; WooldridgeDiD currently rejects `vcov_type="conley"` at `__init__` with a deferral message. Same shape as the SunAbraham / StackedDiD conley follow-ups. | `diff_diff/wooldridge.py` | follow-up | Medium |
 | Extend `WooldridgeDiD` `method ∈ {"logit","poisson"}` paths with `vcov_type ∈ {classical, hc2, hc2_bm}`. The GLM QMLE sandwich uses pseudo-residuals (`weights=p(1-p)` for logit, `weights=μ_i` for Poisson, aweight semantics); composing HC2 leverage and Bell-McCaffrey Satterthwaite DOF with QMLE on canonical-link pseudo-residuals needs derivation + R parity against `clubSandwich::vcovCR(glm(...), type="CR2")`. Phase 1b PR 3/8 rejects `method != "ols" + vcov_type != "hc1"` at `__init__` with a deferral pointer here. | `diff_diff/wooldridge.py` (`_fit_logit`, `_fit_poisson`) | follow-up | Medium |
 | Extend `CallawaySantAnna` with `vcov_type="conley"` — would require deriving a spatial-HAC composition for per-unit influence functions (Conley 1999 spatial kernel × per-(g,t) IF aggregation); no reference implementation exists today. Phase 1b interstitial PR rejected this at `__init__` with a deferral pointer here. | `diff_diff/staggered.py` | follow-up | Low |
 | Extend `TripleDifference` with `vcov_type="conley"` — would require deriving a spatial-HAC composition for the 3-pairwise-DiD influence-function decomposition (Conley 1999 spatial kernel × `inf = w3·IF_3 + w2·IF_2 - w1·IF_1` aggregation); no reference implementation exists today. Phase 1b interstitial #2 PR rejected this at `__init__` with a deferral pointer here. | `diff_diff/triple_diff.py` | follow-up | Low |
 | Extend `ImputationDiD` with `vcov_type="conley"` — would require deriving a spatial-HAC composition with the Theorem 3 per-unit IF aggregation (Conley 1999 spatial kernel × `sigma_sq = (cluster_psi_sums**2).sum()` reduction); no reference implementation exists today. Phase 1b interstitial #3 PR rejected this at `__init__` with a deferral pointer here. | `diff_diff/imputation.py` | follow-up | Low |
+| Extend `EfficientDiD` with `vcov_type="conley"` — would require deriving a spatial-HAC composition with the per-unit EIF aggregation (Conley 1999 spatial kernel × `_compute_se_from_eif` reduction); no reference implementation exists today. Phase 1b interstitial #4 PR rejected this at `__init__` with a deferral pointer here. | `diff_diff/efficient_did.py` | follow-up | Low |
 | Decide whether to formally deprecate `CallawaySantAnna.cluster=X` in favor of `survey_design=SurveyDesign(psu=X)`. Both APIs are first-class today (the bare-cluster path synthesizes a minimal SurveyDesign internally), but having two equivalent paths to express the same intent creates redundant surface. Mirrors a similar question for ImputationDiD / EfficientDiD / TwoStageDiD if those estimators ever face the same review. | `diff_diff/staggered.py` | follow-up | Low |
 | Harmonize SunAbraham's HC1 within-transform finite-sample correction with `fixest::sunab()`. SA's `solve_ols` applies `n / (n - k_dm)` (within-transform columns only); fixest applies `n / (n - k_total)` (counts absorbed FE). SE values differ by ~1-2% on typical panel sizes (documented in REGISTRY.md "Deviation from R"; pinned at `atol=5e-3` in `tests/test_methodology_sun_abraham.py`). Either thread `df_adjustment` into the vcov scaling or document as an intentional difference. | `diff_diff/sun_abraham.py`, `diff_diff/linalg.py::compute_robust_vcov` | follow-up | Low |
 <!-- Rows 104-105 LIFTED 2026-05-20 via the clubSandwich WLS-CR2 port. The diff-diff
@@ -203,7 +204,7 @@ Ordered paydown view across the tables above. Tier A → D is by effort × risk,
 
 #### Tier B — Mid-size methodology (5-10 CI rounds expected, per memory cascade priors)
 
-- Thread `vcov_type` through the 2 remaining standalone estimators: `TwoStageDiD`, `EfficientDiD` (Phase 1b PR 1/8 added SunAbraham, PR 2/8 added StackedDiD, PR 3/8 added WooldridgeDiD-OLS; interstitial #1 narrowed CallawaySantAnna permanently to `{hc1}` per IF-based variance + fixed bare-`cluster=` silent no-op; interstitial #2 narrowed TripleDifference permanently to `{hc1}` per IF-based variance on the 3-pairwise-DiD decomposition; interstitial #3 narrowed ImputationDiD permanently to `{hc1}` per IF-based variance on Theorem 3 per-unit IF aggregation + defensive bootstrap n_psu<2/n_clusters<2 NaN guard)
+- Thread `vcov_type` through the 1 remaining standalone estimator: `TwoStageDiD` (Phase 1b PR 1/8 added SunAbraham, PR 2/8 added StackedDiD, PR 3/8 added WooldridgeDiD-OLS; interstitial #1 narrowed CallawaySantAnna permanently to `{hc1}` per IF-based variance + fixed bare-`cluster=` silent no-op; interstitial #2 narrowed TripleDifference permanently to `{hc1}` per IF-based variance on the 3-pairwise-DiD decomposition; interstitial #3 narrowed ImputationDiD permanently to `{hc1}` per IF-based variance on Theorem 3 per-unit IF aggregation + defensive bootstrap n_psu<2/n_clusters<2 NaN guard; interstitial #4 narrowed EfficientDiD permanently to `{hc1}` per IF-based variance on Chen-Sant'Anna-Xie 2025 EIF aggregation + renamed `EfficientDiDResults.cluster` to `cluster_name` + added `n_clusters`/`vcov_type` fields + `to_dict()` + defensive survey-PSU n<2 NaN guard + eager set_params validation)
 - SyntheticDiD: rename internal `placebo_effects` → `variance_effects` AND public `placebo_effects` field with deprecation alias retained for one release (`synthetic_did.py`, `results.py`)
 - StaggeredTripleDifference R parity: commit CSV fixtures + add covariate-adjusted scenarios + aggregation-SE assertions (`tests/test_methodology_staggered_triple_diff.py`, `benchmarks/R/benchmark_staggered_triplediff.R`)
 - StaggeredTripleDifference: per-cohort group-effect SE WIF override for exact R `triplediff` match (`staggered_triple_diff.py`)

diff --git a/diff_diff/diagnostic_report.py b/diff_diff/diagnostic_report.py
@@ -2441,7 +2441,7 @@ def _pt_hausman(self) -> Dict[str, Any]:
         fit_anticipation = getattr(r, "anticipation", None)
         if isinstance(fit_anticipation, (int, float)) and np.isfinite(fit_anticipation):
             hausman_kwargs["anticipation"] = int(fit_anticipation)
-        fit_cluster = getattr(r, "cluster", None)
+        fit_cluster = getattr(r, "cluster_name", None)
         if isinstance(fit_cluster, str) and fit_cluster:
             hausman_kwargs["cluster"] = fit_cluster