docs(todo): cite #682 in the CiC/QDiD deferral and follow-up rows

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_011hismLBFcUbUzvDRq8ruWb

Author: igerber

TODO.md

@@ -47,8 +47,8 @@ generic sparse-FE, QR+SVD rank-detection redundancy, `check_finite` bypass — m
 
 | Issue | Location | Origin | Effort | Priority |
 |-------|----------|--------|--------|----------|
-| ChangesInChanges/QDiD tutorial notebook (2x2 distributional walkthrough: QTE grid, interior range, uniform bands, CiC-vs-QDiD comparison) - deferred from the implementation PR as a documented decision. | `docs/tutorials/` | CiC PR-B | Mid | Low |
-| `practitioner_next_steps()` dedicated handler for `ChangesInChangesResults` (currently falls back to `_handle_generic`, which is safe; a dedicated handler is the established full-integration step, cf. HAD Phase 5). | `diff_diff/practitioner.py` | CiC PR-B | Quick | Low |
+| ChangesInChanges/QDiD tutorial notebook (2x2 distributional walkthrough: QTE grid, interior range, uniform bands, CiC-vs-QDiD comparison) - deferred from the implementation PR as a documented decision. | `docs/tutorials/` | #682 | Mid | Low |
+| `practitioner_next_steps()` dedicated handler for `ChangesInChangesResults` (currently falls back to `_handle_generic`, which is safe; a dedicated handler is the established full-integration step, cf. HAD Phase 5). | `diff_diff/practitioner.py` | #682 | Quick | Low |
 
 ---
 
@@ -110,11 +110,11 @@ Doable in principle, but no current caller and/or explicitly out of paper scope.
 
 | Issue | Location | PR | Priority |
 |-------|----------|----|----------|
-| ChangesInChanges covariates (Melly-Santangelo 2015 QR pipeline: per-cell quantile-regression conditional CDFs -> conditional CiC -> integrate over treated-group covariates). No R parity target exists (the MS Stata code is the only implementation; distinct from Kranker's `cic`); would need simulation-based validation. Reviewed: `docs/methodology/papers/melly-santangelo-2015-review.md`. | `diff_diff/changes_in_changes.py` | - | Low |
-| ChangesInChanges discrete-outcome bounds + DCIC point identification (Athey-Imbens Sections 4/5.2 incl. Imbens-Manski intervals; Kranker's Stata `cic` is the reference). The shipped ties warning marks the boundary of the continuous scope. | `diff_diff/changes_in_changes.py` | - | Low |
-| ChangesInChanges analytical SEs (Athey-Imbens Theorems 5.1-5.3 influence functions, panel 5.5-5.7, Appendix B covariances; needs the footnote-31 boundary density estimator - note the review's suspected half-range/midpoint typo). Bootstrap is the shipped inference. | `diff_diff/changes_in_changes.py` | - | Low |
-| Staggered/multi-period distributional DiD (Athey-Imbens Section 6 / Ciaccio arXiv:2408.01208v2; `ecic` is the staggered event-study CiC lineage - a distinct method from Ciaccio's copula approach, do not conflate). Reviewed: `docs/methodology/papers/ciaccio-2024-review.md`; ROADMAP row is reviewed-deferred pending demand. | `diff_diff/changes_in_changes.py` | - | Low |
-| ChangesInChanges treatment-on-the-controls (Athey-Imbens Theorem 3.2: group-label exchange + negation; no qte equivalent to anchor parity). | `diff_diff/changes_in_changes.py` | - | Low |
+| ChangesInChanges covariates (Melly-Santangelo 2015 QR pipeline: per-cell quantile-regression conditional CDFs -> conditional CiC -> integrate over treated-group covariates). No R parity target exists (the MS Stata code is the only implementation; distinct from Kranker's `cic`); would need simulation-based validation. Reviewed: `docs/methodology/papers/melly-santangelo-2015-review.md`. | `diff_diff/changes_in_changes.py` | #682 | Low |
+| ChangesInChanges discrete-outcome bounds + DCIC point identification (Athey-Imbens Sections 4/5.2 incl. Imbens-Manski intervals; Kranker's Stata `cic` is the reference). The shipped ties warning marks the boundary of the continuous scope. | `diff_diff/changes_in_changes.py` | #682 | Low |
+| ChangesInChanges analytical SEs (Athey-Imbens Theorems 5.1-5.3 influence functions, panel 5.5-5.7, Appendix B covariances; needs the footnote-31 boundary density estimator - note the review's suspected half-range/midpoint typo). Bootstrap is the shipped inference. | `diff_diff/changes_in_changes.py` | #682 | Low |
+| Staggered/multi-period distributional DiD (Athey-Imbens Section 6 / Ciaccio arXiv:2408.01208v2; `ecic` is the staggered event-study CiC lineage - a distinct method from Ciaccio's copula approach, do not conflate). Reviewed: `docs/methodology/papers/ciaccio-2024-review.md`; ROADMAP row is reviewed-deferred pending demand. | `diff_diff/changes_in_changes.py` | #682 | Low |
+| ChangesInChanges treatment-on-the-controls (Athey-Imbens Theorem 3.2: group-label exchange + negation; no qte equivalent to anchor parity). | `diff_diff/changes_in_changes.py` | #682 | Low |
 | Rust-backend CR2 Bell-McCaffrey port (`return_dof` in the Rust vcov dispatch + CR2 algebra) — **premise re-scoped 2026-07-09**: the scores-based DOF + low-rank factored `A_g` changes made the NumPy CR2-BM path BLAS-bound (`O(n_g k²)` per cluster; 4.1s→38ms at n=100k/k=40), so a Rust port buys ~nothing and adds a parity surface. Revisit only if profiling shows CR2-BM hot again. | `rust/src/linalg.rs` | — | Low |
 | Clustered-CR1 inference df **default flip to `"cluster"` (G−1) at v4** — the opt-in `df_convention=` knob landed 2026-07 (DiD/TWFE/MPD + LinearRegression; REGISTRY §TwoWayFixedEffects deviation note); the remaining work is the major-version default change (moves every clustered p-value/CI) + migration note + flipping `TestDfConvention`/`test_moderate_t_pins_residual_df_convention` expectations. Also evaluate extending the knob to standalone estimators with CR1-t inference at that time. | `diff_diff/linalg.py::LinearRegression`, `diff_diff/estimators.py`, `diff_diff/twfe.py` | — | Medium |
 | CallawaySantAnna **unbalanced-panel R parity — LANDED** via `allow_unbalanced_panel=True` (matches R `did::att_gt(allow_unbalanced_panel=TRUE)` / `DRDID::reg_did_rc`: ATT bit-exact on cells AND dynamic aggregation via fixed unit-cohort-mass `pg` + a per-unit WIF; SE up to the documented CR1 `sqrt(G/(G-1))` factor). The earlier "weighting" framing was a mis-diagnosis — on unbalanced panels the dominant divergence from R is the *estimator* (within-cell differencing vs RC-on-pooled-obs), not only the weighting; both are resolved by the flag. The DEFAULT path keeps within-cell differencing as a documented design choice and now emits a `UserWarning` on unbalanced input (no-silent-failures). **Remaining deferred:** `survey_design=` × `allow_unbalanced_panel=` (per-obs vs per-unit weight resolution — currently fail-closed `NotImplementedError`); and covariate / ipw / dr × the flag R-parity verification (the RC path supports them; the committed golden covers `reg` no-cov). | `staggered.py`, `staggered_aggregation.py` | SE-audit D3 | Low |

diff_diff/__init__.py

@@ -47,6 +47,10 @@
     chaisemartin_dhaultfoeuille,
     twowayfeweights,
 )
+from diff_diff.chaisemartin_dhaultfoeuille_results import (
+    ChaisemartinDHaultfoeuilleResults,
+    DCDHBootstrapResults,
+)
 from diff_diff.changes_in_changes import (
     ChangesInChanges,
     QDiD,
@@ -55,10 +59,6 @@
     ChangesInChangesResults,
     QDiDResults,
 )
-from diff_diff.chaisemartin_dhaultfoeuille_results import (
-    ChaisemartinDHaultfoeuilleResults,
-    DCDHBootstrapResults,
-)
 from diff_diff.continuous_did import (
     ContinuousDiD,
     ContinuousDiDResults,