docs(todo): drop two resolved Actionable rows

- Rust clustered-vcov run-to-run nondeterminism: root-caused and fixed
  by #653 (SipHash HashMap iteration order -> first-appearance remap;
  50 identical calls now bit-identical). The row deferred its own
  deletion until the fix merged; #653 merged 2026-07-08.
- HonestDiD Delta^SD optimal-FLCI center parity: the row was already
  marked LANDED (SE-audit B2b, #635) with Effort 'Done' - it documented
  completed work, not remaining work.

No code changes; CHANGELOG untouched per convention for TODO-hygiene
commits (the underlying fixes carried their own entries).

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

Author: igerber

TODO.md

@@ -30,9 +30,7 @@ The `Origin` column (Actionable tables) and the `PR` column (Deferred tables) bo
 |-------|----------|--------|--------|----------|
 | `SyntheticControl` conformal (CWZ 2021) extensions: (a) one-sided / signed-`t` variants (§7); (b) covariates in the conformal proxy (`X_jt`, eqs 4/6 — current proxy is outcomes-only); (c) AR / innovation-permutation path (Lemmas 5-7) for time-series proxies. The joint test, pointwise CIs, and average-effect CI have landed. | `conformal.py`, `synthetic_control_results.py` | CWZ-2021 | Heavy | Low |
 | `ContinuousDiD` CGBS-2024 extensions. (a) `covariates=` kwarg — **DONE (reg/dr)**; (b) discrete-treatment saturated regression (`treatment_type="discrete"`) — **DONE**; (c) lowest-dose-as-control per Remark 3.1 when `P(D=0)=0` (`control_group="lowest_dose"`) — **DONE** (discrete + continuous mass-point, single-cohort; estimand `ATT(d)−ATT(d_L)`; see REGISTRY Note #7). Remaining (all deferred `NotImplementedError`, documented): `estimation_method="ipw"` on the dose curve (scalar-adjustment / degenerate); `covariates=` × `survey_design=` (weighted OR + weighted nuisance IF); multi-cohort **heterogeneous-support** discrete aggregation (support-aware: average each dose only over the cohorts that observe it); **multi-cohort `lowest_dose`** (within-cohort `d_L` reference + support-aware cross-cohort aggregation); and **`covariates=` × `lowest_dose`** (conditional-PT-relative-to-`d_L` estimand). Single-cohort / 2-period / shared-support multi-cohort are supported. | `continuous_did.py` | CGBS-2024 | Heavy | Low |
-| **Rust-backend `solve_ols(return_vcov=True)` is run-to-run NONDETERMINISTIC** (~1e-14 rel): 8 identical clustered-vcov calls in one process produced 3 distinct values (observed 2026-07-07, Apple Silicon M4, `maturin --features accelerate`; the pure-Python backend is bit-stable run-to-run). Same-seed reproducibility is a documented library expectation, and per the Python-canonical convention the Rust port should at minimum be deterministic. Likely rayon/threaded-BLAS reduction ordering in the faer SVD solve or the vcov meat; investigate and either force a deterministic reduction path or document the wobble as a platform contract with an explicit note. Repro: `solve_ols(X(400x3), y, cluster_ids=10x40, return_vcov=True)` in a loop, compare `vcov[1,1]` bit patterns. | `rust/src/linalg.rs::solve_ols`, `diff_diff/linalg.py` | wild-bootstrap follow-up | Mid | Medium |
 | TWFE's HC2/HC2-BM inline full-dummy build (`twfe.py:280-315`) duplicates the dummy-construction logic in `DifferenceInDifferences(fixed_effects=...)` (`estimators.py:478-486`). Extract a shared helper, or delegate TWFE's HC2/HC2-BM path to DiD's `fixed_effects=` branch (with TWFE-specific cluster-default threading), to reduce drift risk on FE naming / survey behavior / result-surface conventions. Substantive refactor — touches both estimators. | `twfe.py::fit`, `estimators.py::DifferenceInDifferences.fit` | follow-up | Heavy | Low |
-| `HonestDiD` Δ^SD optimal-FLCI center parity — **LANDED (SE-audit B2b)**: replaced the flat Nelder-Mead affine-estimator optimizer with a faithful port of R `HonestDiD::findOptimalFLCI`'s nested convex program (inner min-worst-case-bias at fixed estimator SD `h` via SLSQP QCQP; outer grid-zoom over `h`). Matches R's center + half-length + optimalVec to ~1e-3 (median ~1e-5) across a stress grid; the prior ~9% intermediate-M center drift is removed (widths always matched, coverage unaffected). Analytical folded-normal cv is more accurate than R's MC `.qfoldednormal`. Golden `honest_flci_golden.json` + `TestHonestFLCIParityR`. | `honest_did.py::_flci_solve` | SE-audit | Done | Low |
 
 ### Performance