feat(continuous-did): lowest-dose-as-control (control_group="lowest_dose", Remark 3.1)

CHANGELOG.md

@@ -8,6 +8,19 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 ## [Unreleased]
 
 ### Added
+- **`ContinuousDiD` lowest-dose-as-control** (`control_group="lowest_dose"`, CGBS 2024 Remark 3.1) for
+  settings with no untreated group (`P(D=0) = 0`): the lowest-dose group `d_L` becomes the comparison
+  and the estimand is `ATT(d) − ATT(d_L)` (with `ATT(d_L) = 0` the omitted reference). It is a
+  control-group swap — the entire linear influence-function / bootstrap / event-study / survey
+  machinery is reused unchanged (`ee_control` already carries the reference-group variance) — so both
+  the discrete (`treatment_type="discrete"`) and continuous (B-spline) paths are supported; the
+  discrete ACRT backward-difference reference shifts from `0` to `d_L`
+  (`ACRT(d_1) = ATT(d_1)/(d_1 − d_L)`). The continuous path requires a genuine mass point at the
+  minimum dose (`P(D=d_L) > 0`). Results gain a `reference_dose` field (`= d_L`). Fail-closed:
+  never-treated units present, a singleton `d_L`, no treated dose above `d_L`, `dvals ≤ d_L`, or a
+  survey/subpopulation design leaving `< 2` positive-weight `d_L` units all raise; multi-cohort and
+  `covariates=` × `lowest_dose` raise `NotImplementedError` (deferred). The default `never_treated` /
+  `not_yet_treated` paths are unchanged.
 - **`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

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)**; (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 |
+| `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 |
 | `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` DR no-covariate per-cell SE still uses the ddof=1 plug-in `sqrt(var_t/n_t + var_c/n_c)` rather than the IF-based `sqrt(sum(phi^2))` the reg/ipw paths switched to (O(1/n) from R's `drdid_panel`; aggregated SEs unaffected — they already consume the IF). Switch for method-uniform per-cell/aggregated consistency. | `staggered.py::_doubly_robust` (no-cov branch) | CS-scaling | Quick | Low |