todo: drain items 2/3/4 from Tier A backlog

Removes three Methodology/Correctness rows (EfficientDiD anticipation
trim, generate_ddd_panel_data, TROP fit/_fit_global dedup) and the
three corresponding Tier A bullets, addressed in this PR. Adds two
follow-up rows for the deferred scope (TROP bootstrap-loop dedup,
TripleDifference power auto-routing).

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

Author: igerber

TODO.md

@@ -82,11 +82,10 @@ Deferred items from PR reviews that were not addressed before merge.
 | CallawaySantAnna: consider materializing NaN entries for non-estimable (g,t) cells in group_time_effects dict (currently omitted with consolidated warning); would require updating downstream consumers (event study, balance_e, aggregation) | `staggered.py` | #256 | Low |
 | ImputationDiD dense `(A0'A0).toarray()` scales O((U+T+K)^2), OOM risk on large panels | `imputation.py` | #141 | Medium (deferred — only triggers when sparse solver fails) |
 | Multi-absorb weighted demeaning needs iterative alternating projections for N > 1 absorbed FE with survey weights; unweighted multi-absorb also uses single-pass (pre-existing, exact only for balanced panels) | `estimators.py` | #218 | Medium |
-| EfficientDiD `control_group="last_cohort"` trims at `last_g - anticipation` but REGISTRY says `t >= last_g`. With `anticipation=0` (default) these are identical. With `anticipation>0`, code is arguably more conservative (excludes anticipation-contaminated periods). Either align REGISTRY with code or change code to `t < last_g` — needs design decision. | `efficient_did.py` | #230 | Low |
-| TripleDifference power: `generate_ddd_data` is a fixed 2×2×2 cross-sectional DGP — no multi-period or unbalanced-group support. Add a `generate_ddd_panel_data` for panel DDD power analysis. | `prep_dgp.py`, `power.py` | #208 | Low |
 | Survey design resolution/collapse patterns are inconsistent across panel estimators — ContinuousDiD rebuilds unit-level design in SE code, EfficientDiD builds once in fit(), StackedDiD re-resolves on stacked data; extract shared helpers for panel-to-unit collapse, post-filter re-resolution, and metadata recomputation | `continuous_did.py`, `efficient_did.py`, `stacked_did.py` | #226 | Low |
 | Survey-weighted Silverman bandwidth in EfficientDiD conditional Omega* — `_silverman_bandwidth()` uses unweighted mean/std for bandwidth selection; survey-weighted statistics would better reflect the population distribution but is a second-order refinement | `efficient_did_covariates.py` | — | Low |
-| TROP: `fit()` and `_fit_global()` share ~150 lines of near-identical data setup (panel pivoting, absorbing-state validation, first-treatment detection, effective rank, NaN warnings). Both bootstrap methods also duplicate the stratified resampling loop. Extract shared helpers to eliminate cross-file sync risk. | `trop.py`, `trop_global.py`, `trop_local.py` | — | Low |
+| TROP: extend Wave 4's `_setup_trop_data` helper to also cover the duplicated bootstrap resampling loop in `_bootstrap_variance` / `_bootstrap_variance_global` (~40 LoC dedup; mirrors the data-setup helper pattern with a `fit_callable` parameter for the per-draw refit step). | `trop_local.py`, `trop_global.py` | follow-up | Low |
+| TripleDifference power auto-routing: `power.simulate_power` ignores `n_periods` for DDD because `_ddd_dgp_kwargs` is hard-coded to the cross-sectional `generate_ddd_data`. Now that `generate_ddd_panel_data` exists (Wave 4), add a new `_EstimatorProfile` registry entry (or extend the existing one) to route to the panel DGP when `n_periods > 2`. | `power.py`, `prep_dgp.py` | follow-up | Low |
 | StaggeredTripleDifference R cross-validation: 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 |
 | StaggeredTripleDifference R parity: benchmark only tests no-covariate path (xformla=~1). Add covariate-adjusted scenarios and aggregation SE parity assertions. | `benchmarks/R/benchmark_staggered_triplediff.R` | #245 | Medium |
 | StaggeredTripleDifference: per-cohort group-effect SEs include WIF (conservative vs R's wif=NULL). Documented in REGISTRY. Could override mixin for exact R match. | `staggered_triple_diff.py` | #245 | Low |
@@ -170,9 +169,6 @@ Ordered paydown view across the tables above. Tier A → D is by effort × risk,
 
 #### Tier A — Quick wins (≤1 day, ≤3 CI rounds expected)
 
-- EfficientDiD `control_group="last_cohort"` REGISTRY-vs-code alignment with `anticipation>0` (`efficient_did.py`, one design decision)
-- TripleDifference: add `generate_ddd_panel_data` for panel DDD power analysis (`prep_dgp.py`, `power.py`)
-- TROP: extract shared data-setup helper between `fit()` and `_fit_global()` (~150 LoC dedup; `trop.py`, `trop_global.py`, `trop_local.py`)
 - WooldridgeDiD: optional efficiency hint when method/outcome pairing is sub-optimal (NOT a canonical-link violation per W2023 Prop 3.1 — see Methodology/Correctness row for the corrected framing)
 
 (SyntheticDiD `placebo_effects` → `variance_effects` rename moved to Tier B — the user-facing field rename + one-release deprecation alias is too large for ≤1 day / ≤3 CI rounds.)