igerber · Oct 23, 2026 · Oct 21, 2026
diff --git a/.claude/commands/docs-check.md b/.claude/commands/docs-check.md
@@ -50,7 +50,6 @@ The following estimators/features MUST have documentation:
 | ContinuousDiD | "ContinuousDiD" | "ContinuousDiD" | continuous_did.rst | "Multi-Period and Staggered" |
 | HeterogeneousAdoptionDiD | "HeterogeneousAdoptionDiD" | "HeterogeneousAdoptionDiD" | had.rst | "Heterogeneous Adoption (No-Untreated Designs)" |
 | TripleDifference | "TripleDifference" | "TripleDifference" | triple_diff.rst | "Triple Difference" |
-| StaggeredTripleDifference | "StaggeredTripleDifference" | "StaggeredTripleDifference" | staggered.rst | "Triple Difference" |
 | WooldridgeDiD | "WooldridgeDiD" | "WooldridgeDiD" | wooldridge_etwfe.rst | "Multi-Period and Staggered" |
 | TROP | "TROP" | "TROP" | trop.rst | "Triply Robust Panel" |
 | HonestDiD | n/a (in `## Diagnostics`) | n/a (in `## Diagnostics`) | honest_did.rst | "Honest DiD" |

diff --git a/.claude/memory.md b/.claude/memory.md
@@ -98,9 +98,9 @@ Target contract: `docs/v4-design.md` section 5; per-surface lifecycle:
   were removed at 4.0)
 - `time` calendar period everywhere; DifferenceInDifferences /
   TripleDifference take the 0/1 post dummy as `post` (the 3.x `time=`
-  spelling on DiD was removed at 4.0, M-030; on TripleDifference it is
-  still a warning shim, M-031, because `time` persists as the merged
-  class's staggered calendar column - its 4.0 enforcement is M-085);
+  spelling on DiD was removed at 4.0, M-030; on TripleDifference the name
+  `time` persists as the staggered calendar column, M-031, and in 2x2x2
+  mode `time=` raises naming `post=` since 4.0 PR 4a, M-085);
   static TwoWayFixedEffects takes `post` and its `time` is the event-study
   calendar column (the static `time=` alias on TWFE is the same kind of
   surviving shim, M-082, enforced by M-083)

diff --git a/DEFERRED.md b/DEFERRED.md
@@ -57,7 +57,7 @@ exists but parity can't be verified without a local toolchain.
 
 | Issue | Location | PR | Priority |
 |-------|----------|----|----------|
-| `StaggeredTripleDifference` R cross-validation (the engine is shared with `TripleDifference`'s staggered mode since 3(b), so this covers both surfaces): CSV fixtures not committed (gitignored); tests skip without local R + `triplediff`. Commit fixtures or generate deterministically. | `tests/test_methodology_staggered_triple_diff.py` | #245 | Medium |
+| Staggered-DDD R cross-validation (`TripleDifference`'s staggered mode; the engine formerly behind `StaggeredTripleDifference`): CSV fixtures not committed (gitignored); tests skip without local R + `triplediff`. Commit fixtures or generate deterministically. | `tests/test_methodology_staggered_triple_diff.py` | #245 | Medium |
 | Staggered DDD R parity (both surfaces - one shared engine since 3(b)): benchmark only tests the no-covariate path (`xformla=~1`). Add covariate-adjusted scenarios + aggregation-SE parity assertions. | `benchmarks/R/benchmark_staggered_triplediff.R` | #245 | Medium |
 | Staggered DDD per-cohort group-effect SEs include WIF (both surfaces - one shared engine since 3(b)) (conservative vs R's `wif=NULL`); documented in REGISTRY. Could override the mixin for an exact R match (verification needs R `triplediff`). | `_staggered_triple_diff_engine.py` | #245 | Low |
 | **WooldridgeDiD follow-up cluster** (PR-B Stage D/E fail-closed surfaces; re-enable after R/Stata validation): <br>• QMLE sandwich uses `aweight` cluster adjustment `(G/(G-1))·(n-1)/(n-k)` vs Stata's `G/(G-1)` (conservative); add a `qmle` weight type if Stata goldens confirm a material difference (`wooldridge.py`, `linalg.py`). <br>• response-scale APE / log-link coefficient bridge for R `etwfe(family=poisson\|logit)` cell-level parity — needs `emfx()` APE extraction or link-inversion with baseline-mean adjustment (`generate_wooldridge_golden.R`, `test_methodology_wooldridge.py`). <br>• `aggregate(weights="cohort_share")` on survey-weighted fits: `_n_g_per_cohort` uses raw `unit.nunique()`; implement design-weighted unit totals per cohort (paper W2025 §7) and lift the `ValueError` gate (`wooldridge.py`, `wooldridge_results.py`). <br>• unconditional inference for `cohort_share` accounting for ω̂_g sampling uncertainty (W2025 §7.5); currently NaN-closed (`wooldridge_results.py`). <br>• `cohort_trends=True × survey_design` and `× control_group="never_treated"` raise `NotImplementedError` (unvalidated TSL variance / trend columns spanned jointly by the placebo cells and the unit FE, which absorb the cohort indicator and recover the omitted reference) (`wooldridge.py`). <br>• ~~Stata `jwdid` golden-value `TestReferenceValues`~~ RESOLVED: the golden ships four arms pinned by `tests/test_etwfe_cs_stata_parity.py` (no `TestReferenceValues` symbol was ever added). The QMLE bullet above REMAINS OPEN -- every golden arm is linear `jwdid`, so no QMLE cluster-SE reference exists, and SEs are pinned only as a ratio. | `wooldridge.py`, `wooldridge_results.py`, `linalg.py`, benchmarks | #216 · PR-B | Med-Low |
@@ -143,7 +143,7 @@ decisions (refactor waivers, perf trade-offs, test-infrastructure calls) are rec
 
 | Decision | Location | Verified |
 |----------|----------|----------|
-| **`utils.validate_pscore_trim` ships without the `allow_zero` flag the retired TODO row proposed.** The row (retired in the M-145 PR) suggested `validate_pscore_trim(value, *, allow_zero)`; after ContinuousDiD's alignment to `0 < x < 0.5`, no consumer needs `allow_zero=True` — every migrated copy (TripleDifference, DMLDiD, ContinuousDiD, LWDiD, CallawaySantAnna) wants the strict interval, and the deliberately-unmigrated `StaggeredTripleDifference` keeps its own bare check rather than a flagged call. A dead-on-arrival parameter on a shared validator is a drift magnet; re-add the flag only when a real caller needs a `[0, 0.5)` domain, with a distinct range message | `diff_diff/utils.py` | M-145 / 2026-08-27 |
+| **`utils.validate_pscore_trim` ships without the `allow_zero` flag the retired TODO row proposed.** The row (retired in the M-145 PR) suggested `validate_pscore_trim(value, *, allow_zero)`; after ContinuousDiD's alignment to `0 < x < 0.5`, no consumer needs `allow_zero=True` — every migrated copy (TripleDifference, DMLDiD, ContinuousDiD, LWDiD, CallawaySantAnna) wants the strict interval, and the former `StaggeredTripleDifference` class's bare check went with the class (PR 4a) rather than becoming a flagged call. A dead-on-arrival parameter on a shared validator is a drift magnet; re-add the flag only when a real caller needs a `[0, 0.5)` domain, with a distinct range message | `diff_diff/utils.py` | M-145 / 2026-08-27 |
 | **Fixed-B Stata SE-golden comparisons in the LWDiD suite run unmarked in default CI** (no `ci_params.bootstrap()` scaling, no `slow` marker): the SE tolerance derives from BOTH fixed rep counts (ours B=999, Stata's R from the golden meta) so scaling either side would invalidate the committed-golden comparison — the ci_params convention governs convergence-style tests, not fixed-B golden comparisons — and these tests are the PR #588 acceptance bar, which must run in default CI; module-scoped fit memoization bounds the cost (~1-2 s per fit locally) | `tests/test_methodology_lwdid.py` | 2026-08-16 |
 | **The 4.0 migration guide's code blocks are not snippet-executed.** `tests/test_doc_snippets.py` discovers a hardcoded list of `.rst` files and only `.. code-block:: python` / RST `::` bodies, so `docs/migration-4.0.md` gets no coverage. Deliberate: the guide is a MIXED document - most "after" examples (the renames, `results.att`) run on the current release, but the `field-flip` and `df-convention-flip` examples describe 4.0 behaviour that cannot run until 4.0, so a blanket execution lane would fail by construction. Closing the gap fully means a markdown-fence extractor plus a skip-marker convention for the future-API blocks - a harness change, out of scope for a docs PR. What IS gated: the appendix's ledger parity (`test_migration_guide_*`), which pins the row set and every mechanically checkable cell; and, since the first local review found all three merge examples carrying invalid keywords, `test_migration_guide_examples_bind_to_real_signatures`, which ast-parses the guide's python blocks and asserts every constructor/`fit()` keyword exists on the target signature. That is signature binding, NOT execution - it deliberately skips calls whose owner it cannot resolve (e.g. `results.aggregate(...)`), and it cannot catch a wrong VALUE or a wrong sequence of calls. The hand-written `Fix` prose remains unverifiable by any available means. | `docs/migration-4.0.md`, `tests/test_doc_snippets.py` | Phase 4 / 2026-08-09 |
 | **MMM launch carousel carries scoped marketing claims, not exporter-contract documentation.** The deck (a LinkedIn marketing artifact, not a docs surface) states capabilities in scoped-but-punchy form: guardrail copy says "the easy mistakes fail loudly" / "you own the design, it owns the math" (the caller-owned estimand/population/window/outcome-scale alignment lives in REGISTRY.md's MMM section and the exporter docstrings, not on slides), and slide 8 shows tutorial 29's worked staggered-boost lift row WITHOUT an on-slide linearity qualifier - the compression's linear-channel scoping is the tutorial's job, which the CTA points to. Absolutes ("no silent mis-calibration", "any estimate exports", "anything with an estimate + SE") are ban-listed by `tests/test_mmm_carousel_claims.py`; local review R1/R2 pressed for compliance-style scoping language on the slides and the marketing-appropriate scoped copy was chosen instead. | `carousel/generate_mmm_carousel.py`, `tests/test_mmm_carousel_claims.py` | mmm-carousel / 2026-08-20 |

diff --git a/METHODOLOGY_REVIEW.md b/METHODOLOGY_REVIEW.md
@@ -66,7 +66,7 @@ The catalog grew incrementally over several quarters, so formats vary across the
 | Estimator | Module | R Reference | Status | Last Review |
 |-----------|--------|-------------|--------|-------------|
 | TripleDifference | `triple_diff.py` | `triplediff::ddd()` | **Complete** | 2026-02-18 |
-| StaggeredTripleDifference | `staggered_triple_diff.py` | `triplediff::ddd(panel=TRUE)` + `agg_ddd()` | **Complete** | 2026-05-30 |
+| TripleDifference (staggered mode; the former StaggeredTripleDifference class) | `_staggered_triple_diff_engine.py` | `triplediff::ddd(panel=TRUE)` + `agg_ddd()` | **Complete** | 2026-05-30 |
 
 ### Counterfactual / Synthetic Estimators
 
@@ -988,7 +988,7 @@ These three are feature deferrals (paper-supported extensions that the library h
 
 | Field | Value |
 |-------|-------|
-| Module | `_staggered_triple_diff_engine.py` (the shared engine), `staggered_triple_diff.py` (deprecated class surface), `staggered_triple_diff_results.py` |
+| Module | `_staggered_triple_diff_engine.py` (the engine behind `TripleDifference`'s staggered mode; the `staggered_triple_diff.py` class surface was removed at 4.0), `staggered_triple_diff_results.py` |
 | Primary Reference | Ortiz-Villavicencio & Sant'Anna (2025) — same paper as TripleDifference, staggered case |
 | R Reference | `triplediff::ddd(panel=TRUE)` + `agg_ddd()` (per `benchmarks/R/benchmark_staggered_triplediff.R`) |
 | Status | **Complete** |
@@ -1464,7 +1464,7 @@ Goldens at `benchmarks/data/r_conleyreg_conley_golden.json`; generator at `bench
 
 **Outstanding Concerns — cross-estimator survey coverage boundary** (intentional, fail-closed `NotImplementedError` deferrals, not bugs; line refs current as of this review — re-grep before relying on them):
 - **Conley + survey_design** (open methodological question — no canonical weighted spatial-HAC under probability sampling): `conley.py:298`, `linalg.py:1367` / `linalg.py:3501`, `spillover.py:3246`.
-- **Replicate-weight designs** (use TSL strata/PSU/FPC, or `n_bootstrap=0`): `synthetic_did.py:437`, `continuous_did.py:2181`, `staggered_triple_diff.py:701`, `spillover.py:2400`, `bacon.py:526`, `had.py:1751` (+ HAD pretests), `wooldridge.py:89`, `trop.py:444`, `staggered.py:2228` (CS bootstrap), `efficient_did.py:1175`, `chaisemartin_dhaultfoeuille.py:2810`.
+- **Replicate-weight designs** (use TSL strata/PSU/FPC, or `n_bootstrap=0`): `synthetic_did.py:437`, `continuous_did.py:2181`, `_staggered_triple_diff_engine.py` (replicate + bootstrap guard), `spillover.py:2400`, `bacon.py:526`, `had.py:1751` (+ HAD pretests), `wooldridge.py:89`, `trop.py:444`, `staggered.py:2228` (CS bootstrap), `efficient_did.py:1175`, `chaisemartin_dhaultfoeuille.py:2810`.
 - **Survey + non-HC1 vcov** — HC2 / HC2-BM / classical **explicitly raise `NotImplementedError`** under `survey_design=` (a fail-closed guard: the survey TSL / replicate-refit variance would otherwise silently discard the requested sandwich family): `stacked_did.py:428`, `sun_abraham.py:751`, `wooldridge.py:702`, `twfe.py:252`.
 - **Survey + user `cluster=`** — **explicitly raises** (a fail-closed guard, not a silent drop): the survey TSL / replicate-refit variance would otherwise ignore `cluster=`, so the combination is rejected at construction: `efficient_did.py:518`, `staggered.py:1719`, `imputation.py:314`, `two_stage.py:1439`, `triple_diff.py:674`.
 - **SyntheticControl** — no survey support yet (`synthetic_control.py:335`).

diff --git a/README.md b/README.md
@@ -109,14 +109,13 @@ Full guide: `diff_diff.get_llm_guide("practitioner")`.
 - [SpilloverDiD](https://diff-diff.readthedocs.io/en/stable/api/spillover.html) - Butts (2021) ring-indicator spillover-aware DiD identifying direct effect on treated + per-ring spillover on near-control units; handles non-staggered and staggered timing; supports survey-design variance under `survey_design=` for HC1 / CR1 (Wave E.1 Binder TSL) and Conley (Wave E.2 panel-aware stratified-Conley sandwich on per-period PSU totals; extended in Wave E.2 follow-up to `conley_lag_cutoff > 0` via panel-block composition with within-PSU serial Bartlett HAC — `lag>0` requires an effective PSU via explicit `survey_design.psu` or injected `cluster=<col>`); `SurveyDesign.subpopulation()` preserves full-design `n_psu` / `df_survey` via zero-padded scores (Wave E.3, R `svyrecvar(subset())` form)
 - [SyntheticDiD](https://diff-diff.readthedocs.io/en/stable/api/estimators.html) - Synthetic DiD combining standard DiD and synthetic control for few treated units
 - [SyntheticControl](https://diff-diff.readthedocs.io/en/stable/api/synthetic_control.html) - Abadie, Diamond & Hainmueller (2010) classic synthetic control for a single treated unit (donor-weight counterfactual, nested/cv/inverse-variance/custom V; in-space placebo permutation inference via `in_space_placebo()`, plus ADH-2015 `leave_one_out()` + `in_time_placebo()` robustness, Firpo-Possebom (2018) test-inversion confidence sets, and Chernozhukov-Wüthrich-Zhu (2021) conformal inference)
-- [TripleDifference](https://diff-diff.readthedocs.io/en/stable/api/triple_diff.html) - triple difference (DDD) estimator for designs requiring two criteria for treatment eligibility; serves both the 2x2x2 and the staggered-adoption design from one signature (`fit(..., first_treat=)` selects the staggered engine)
+- [TripleDifference](https://diff-diff.readthedocs.io/en/stable/api/triple_diff.html) - triple difference (DDD) estimator for designs requiring two criteria for treatment eligibility; serves both the 2x2x2 and the staggered-adoption design (Ortiz-Villavicencio & Sant'Anna (2025) staggered DDD with group-time ATT) from one signature (`fit(..., first_treat=)` selects the staggered engine)
 - [ContinuousDiD](https://diff-diff.readthedocs.io/en/stable/api/continuous_did.html) - Callaway, Goodman-Bacon & Sant'Anna (2024) continuous treatment DiD with dose-response curves
 - [HeterogeneousAdoptionDiD](https://diff-diff.readthedocs.io/en/stable/api/had.html) - de Chaisemartin, Ciccia, D'Haultfœuille & Knau (2026) for designs where **no unit remains untreated**; local-linear estimator at the dose support boundary returning Weighted Average Slope (WAS) on Design 1' (`d̲ = 0` / QUG) or `WAS_{d̲}` on Design 1 (`d̲ > 0`, continuous-near-d̲ or mass-point), with a multi-period event-study extension (last-treatment cohort, pointwise CIs). **Panel-only** in this release - repeated cross-sections rejected by the validator. Alias `HAD`.
 - [RegressionDiscontinuity](https://diff-diff.readthedocs.io/en/stable/api/regression_discontinuity.html) - Calonico, Cattaneo & Titiunik (2014) sharp, fuzzy, AND covariate-adjusted regression discontinuity with robust bias-corrected inference and rdrobust-parity bandwidth selection (all 10 selectors, mass-point handling; fuzzy via `takeup=` with a first-stage block and weak-identification warning; covariates via `covariates=` - CCFT 2019, same estimand, covariate-aware bandwidths). Canonical `att` is the bias-corrected estimate with a coherent robust CI (rdrobust's printed headline is `att_conventional`). Alias `RDD`.
 - [StackedDiD](https://diff-diff.readthedocs.io/en/stable/api/stacked_did.html) - Wing, Freedman & Hollingsworth (2024) stacked DiD with Q-weights and sub-experiments; optional covariate balancing (Ustyuzhanin 2026)
 - [EfficientDiD](https://diff-diff.readthedocs.io/en/stable/api/efficient_did.html) - Chen, Sant'Anna & Xie (2025) efficient DiD with optimal weighting for tighter SEs
 - [TROP](https://diff-diff.readthedocs.io/en/stable/api/trop.html) - Triply Robust Panel estimator (Athey et al. 2025) with nuclear norm factor adjustment
-- [StaggeredTripleDifference](https://diff-diff.readthedocs.io/en/stable/api/staggered.html#staggeredtripledifference) - Ortiz-Villavicencio & Sant'Anna (2025) staggered DDD with group-time ATT (deprecated 3.9 - use `TripleDifference` with `first_treat=`)
 - [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, for absorbing or non-absorbing (reversible) treatment
 - [ChangesInChanges](https://diff-diff.readthedocs.io/en/stable/api/changes_in_changes.html) - Athey & Imbens (2006) nonlinear/distributional DiD for the 2x2 design: full counterfactual distribution and quantile treatment effects via CDF transformation, plus the QDiD comparison estimator via `method="qdid"`; bootstrap inference; R qte parity. Alias `CiC`