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Copy file name to clipboardExpand all lines: README.md
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-[SunAbraham](https://diff-diff.readthedocs.io/en/stable/api/staggered.html) - Sun & Abraham (2021) interaction-weighted estimator for heterogeneity-robust event studies
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-[ImputationDiD](https://diff-diff.readthedocs.io/en/stable/api/imputation.html) - Borusyak, Jaravel & Spiess (2024) imputation estimator, most efficient under homogeneous effects
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-[TwoStageDiD](https://diff-diff.readthedocs.io/en/stable/api/two_stage.html) - Gardner (2022) two-stage estimator with GMM sandwich variance
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-[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; `conley_lag_cutoff=0`only — serial Bartlett HAC composition queued as follow-up)
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-[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>`)
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-[SyntheticDiD](https://diff-diff.readthedocs.io/en/stable/api/estimators.html) - Synthetic DiD combining standard DiD and synthetic control for few treated units
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-[TripleDifference](https://diff-diff.readthedocs.io/en/stable/api/triple_diff.html) - triple difference (DDD) estimator for designs requiring two criteria for treatment eligibility
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-[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
Copy file name to clipboardExpand all lines: TODO.md
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|`SyntheticDiD(vcov_type="conley")` support. Currently raises `TypeError` at `__init__` because SyntheticDiD uses `variance_method ∈ {bootstrap, jackknife, placebo}` rather than the analytical sandwich that Conley plugs into. Wiring would require either reimplementing an analytical sandwich path for SyntheticDiD or designing a spatial-block bootstrap (new methodology, Politis-Romano 1994 territory). |`synthetic_did.py::SyntheticDiD`| follow-up (spillover-conley) | Low |
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|`SpilloverDiD(survey_design=...)` replicate-weight variance (BRR / Fay / JK1 / JKn / SDR). Wave E.1 ships Taylor-linearization only. Per Gerber (2026) Appendix A, the IF-reweighting shortcut does NOT apply to TwoStageDiD-class estimators because `gamma_hat` is weight-sensitive; correct support requires per-replicate full re-fit of stage 1 and stage 2 (200+ LoC of test surface beyond E.1). |`spillover.py::SpilloverDiD.fit`, `survey.py::compute_replicate_refit_variance`| follow-up | Low |
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|`SpilloverDiD(survey_design=...)` subpopulation preservation (Wave E.3). Wave E.1's `finite_mask` block physically removes zero-weight rows that lose stage-1 FE support, so `SurveyDesign.subpopulation()`-derived designs see `n_psu` / `df_survey` / Binder centering recomputed on the reduced fit sample rather than the full domain design. Standard domain-estimation practice (R `survey::svyrecvar` on a `subset()` design) preserves the original PSU/strata counts and treats out-of-domain rows as zero-score padding. Fix requires separating fit-sample alignment (Psi array) from design-level bookkeeping: preserve a full-design `resolved_survey` for inference metadata + zero-pad dropped zero-weight rows' IF contribution. Add `SurveyDesign.subpopulation()` regression test to lock the contract. |`spillover.py::SpilloverDiD.fit`, `two_stage.py::_compute_binder_tsl_meat`| follow-up (Wave E.3) | Medium |
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| `SpilloverDiD(vcov_type="conley", conley_lag_cutoff > 0, survey_design=...)` serial Bartlett HAC composition. Wave E.2 ships the panel-aware `conley_lag_cutoff = 0` case ("within-period spatial only" — `sum_t sum_h M_h_t` per `tests/test_spillover.py::TestSpilloverDiDWaveE2ConleySurveyDesign::test_b_panel_aware_per_period_sum_invariant`) and raises `NotImplementedError` upfront at `spillover.py:fit` on `conley_lag_cutoff > 0`. The serial Bartlett component (within-unit / within-PSU temporal HAC at lag ≤ L) needs to compose with the panel-aware stratified-Conley spatial sandwich — the natural addition is `meat_serial = sum_g sum_{|t-s|<=L, t!=s} (1 - |t-s|/(L+1)) * (S_psu_t[g] - S_bar_h_t)(S_psu_s[g] - S_bar_h_s)'` per PSU, summed across all PSUs in each stratum, with appropriate Binder FPC scaling — plus a methodology call on whether to include cross-period spatial pairs in the serial term. Regression goldens vs the cross-sectional limit (lag=0, which is now the shipped path). | `spillover.py::SpilloverDiD.fit`, `two_stage.py::_compute_stratified_conley_meat` | follow-up (Wave E.2 follow-up) | Medium |
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| Serial Bartlett kernel logic duplicated between `diff_diff/two_stage.py::_compute_stratified_serial_bartlett_meat` (survey path) and `diff_diff/conley.py::_compute_conley_meat` panel-block branch (no-survey path). Both compute `K[t,s] = (1 - |t-s|/(L+1)) * 1{|t-s| <= L, t != s}` over dense panel-period codes. Factor out a shared `_serial_bartlett_kernel_matrix(t_codes, L)` helper and a shared post-meat finite + PSD-warning guard so the survey and no-survey paths can't drift on diagnostics or kernel weights. Cosmetic; refactor doesn't change behavior. |`two_stage.py::_compute_stratified_serial_bartlett_meat`, `conley.py::_compute_conley_meat`| follow-up | Low |
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| `SpilloverDiD(vcov_type="conley", conley_lag_cutoff > 0, survey_design=...)` no-effective-PSU serial Bartlett HAC. Wave E.2 follow-up ships the panel-block composition when an effective PSU exists (explicit `survey_design.psu` OR injected via `cluster=<col>` per `_inject_cluster_as_psu`). Weights-only / strata-only survey designs WITHOUT a cluster fallback raise `NotImplementedError` at `SpilloverDiD.fit` post-resolution because under the pseudo-PSU = obs-index fallback each pseudo-PSU appears in exactly one period — the per-PSU serial cross-period loop would silently contribute zero. Fix would either derive a unit-level serial fallback for no-PSU designs (mixes IF allocators with the pseudo-PSU spatial term — needs methodology work) or route the serial loop through `conley_unit` with explicit documentation of the IF-allocator asymmetry. Regression goldens vs the effective-PSU shipped path. | `spillover.py::SpilloverDiD.fit`, `two_stage.py::_compute_stratified_serial_bartlett_meat` | follow-up (Wave E.2 follow-up tail) | Low |
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|`SpilloverDiD(ring_method="count")` extension. Currently only the nearest-treated-ring specification is exposed. Count-of-treated-in-ring (paper Section 3.2 end) is methodologically supported by Butts but re-introduces functional-form dependence; expose with an explicit kwarg gate and documentation warning. |`spillover.py::SpilloverDiD.fit`| follow-up | Low |
Copy file name to clipboardExpand all lines: diff_diff/guides/llms.txt
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- [SunAbraham](https://diff-diff.readthedocs.io/en/stable/api/staggered.html): Sun & Abraham (2021) interaction-weighted estimator for heterogeneity-robust event studies
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- [ImputationDiD](https://diff-diff.readthedocs.io/en/stable/api/imputation.html): Borusyak, Jaravel & Spiess (2024) imputation estimator — most efficient under homogeneous effects
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- [TwoStageDiD](https://diff-diff.readthedocs.io/en/stable/api/two_stage.html): Gardner (2022) two-stage estimator with GMM sandwich variance
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- [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-control; reuses `conley_coords` for ring construction; handles non-staggered and staggered timing; supports `SurveyDesign(weights, strata, psu, fpc)` under `vcov_type="hc1"` with optional `cluster=<col>` for CR1 via Gerber (2026) Binder TSL (Wave E.1) and under `vcov_type="conley"` via a panel-aware stratified-Conley sandwich on per-period PSU totals (Wave E.2; `conley_lag_cutoff=0` only — serial Bartlett HAC composition queued as follow-up), both composed with the Wave D Gardner GMM correction (replicate weights queued as follow-up)
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- [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-control; reuses `conley_coords` for ring construction; handles non-staggered and staggered timing; supports `SurveyDesign(weights, strata, psu, fpc)` under `vcov_type="hc1"` with optional `cluster=<col>` for CR1 via Gerber (2026) Binder TSL (Wave E.1) and under `vcov_type="conley"` via a panel-aware stratified-Conley sandwich on per-period PSU totals (Wave E.2 cross-sectional `conley_lag_cutoff=0`) extended in Wave E.2 follow-up to `conley_lag_cutoff > 0` via panel-block composition with within-PSU serial Bartlett HAC (Newey-West 1987 separable form; `lag>0` requires an effective PSU via explicit `survey_design.psu` or injected `cluster=<col>`), both composed with the Wave D Gardner GMM correction (replicate weights queued as follow-up)
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- [SyntheticDiD](https://diff-diff.readthedocs.io/en/stable/api/estimators.html): Synthetic DiD combining standard DiD and synthetic control methods for few treated units
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- [TripleDifference](https://diff-diff.readthedocs.io/en/stable/api/triple_diff.html): Triple difference (DDD) estimator for designs requiring two criteria for treatment eligibility
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- [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
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