feat(continuous-did): discrete-treatment saturated regression (treatment_type="discrete")

CHANGELOG.md

@@ -8,6 +8,16 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 ## [Unreleased]
 
 ### Added
+- **`ContinuousDiD` discrete-treatment saturated regression** (`treatment_type="discrete"`) for
+  multi-valued / discrete dose (CGBS 2024 Eq. 4.1). Each distinct dose level gets its own effect
+  coefficient — `ATT(d_j) = mean_{D=d_j}(ΔY) − control` (a per-level 2×2 DiD) — instead of a B-spline
+  curve; `ACRT(d_j)` is the paper's backward finite difference on the grid `{0, d_1, …, d_J}`
+  (`ACRT(d_1) = ATT(d_1)/d_1`, so a binary `D ∈ {0,1}` gives `ACRT = ATT`). The saturated fit is an
+  exact basis swap, so analytical, multiplier-bootstrap, covariate (`reg`/`dr`), and survey inference
+  all compose and reduce analytically to the per-level 2×2 DiD standard error.
+  Multi-cohort fits with heterogeneous dose support across cohorts raise `NotImplementedError`
+  (support-aware aggregation deferred); an off-support `dvals` value raises `ValueError`. The default
+  `treatment_type="continuous"` (B-spline) path is unchanged.
 - **`ContinuousDiD` covariate support** (`covariates=`, `estimation_method ∈ {"reg", "dr"}`) for
   dose-response estimation under **conditional** parallel trends
   (`E[ΔY(0) | D=d, X] = E[ΔY(0) | D=0, X]`). `reg` uses an outcome-regression control counterfactual;

TODO.md

@@ -29,7 +29,7 @@ The `Origin` column (Actionable tables) and the `PR` column (Deferred tables) bo
 | Issue | Location | Origin | Effort | Priority |
 |-------|----------|--------|--------|----------|
 | `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)**; remaining: (b) discrete-treatment saturated regression (integer dose currently warned, not routed to per-level coefficients); (c) lowest-dose-as-control per Remark 3.1 when `P(D=0)=0`. Also deferred from the covariate work: `estimation_method="ipw"` on the dose curve (scalar-adjustment / degenerate — documented `NotImplementedError`), and `covariates=` × `survey_design=` (weighted OR + weighted nuisance IF). | `continuous_did.py` | CGBS-2024 | Heavy | Low |
+| `ContinuousDiD` CGBS-2024 extensions. (a) `covariates=` kwarg — **DONE (reg/dr)**; (b) discrete-treatment saturated regression (`treatment_type="discrete"`) — **DONE**; remaining: (c) lowest-dose-as-control per Remark 3.1 when `P(D=0)=0`. Also deferred: `estimation_method="ipw"` on the dose curve (scalar-adjustment / degenerate — documented `NotImplementedError`); `covariates=` × `survey_design=` (weighted OR + weighted nuisance IF); and multi-cohort **heterogeneous-support** discrete aggregation (support-aware: average each dose only over the cohorts that observe it — currently `NotImplementedError`; single-cohort / 2-period / shared-support multi-cohort are supported). | `continuous_did.py` | CGBS-2024 | Heavy | Low |
 | `ImputationDiD` LOO conservative-variance refinement (BJS 2024 Supp. Appendix A.9) — a finite-sample improvement to the auxiliary-model residuals reducing overfit of `tau_tilde_g` to `epsilon`. Asymptotic Theorem-3 variance is implemented and matches R `didimputation` (which also omits LOO by default). | `imputation.py` | imputation-validation | Mid | Low |
 | `TwoWayFixedEffects(vcov_type in {hc2, hc2_bm})` with replicate-weight designs raises `NotImplementedError` (`twfe.py:~233`). The replicate path re-demeans per replicate, which doesn't compose with the full-dummy HC2/HC2-BM build — a correct impl needs per-replicate full-dummy refit. Workaround: `hc1` for replicate-weight CR1. | `twfe.py::fit` | follow-up | Heavy | Low |
 | `CallawaySantAnna` reg-method aggregated SEs sit 3-20% from the R golden fixtures (`two_period` simple/group/dynamic, `dynamic_effects` dynamic) while the ATTs match at 1e-11 and the dr-method aggregated SEs match at ≤7e-6. Pre-existing (byte-identical deltas on the pre-fast-path tree); golden aggregated-SE assertions were therefore enabled for dr scenarios only (`test_golden_simple_aggregation_se`, `test_golden_event_study_aggregation_se`). Investigate the reg-path per-cell IF vs R `DRDID::reg_did_panel`'s. | `staggered.py::_outcome_regression`, `tests/test_csdid_ported.py` | CS-scaling | Mid | Medium |