diff --git a/TODO.md b/TODO.md index 8cc3475c..0f44c662 100644 --- a/TODO.md +++ b/TODO.md @@ -86,6 +86,11 @@ Deferred items from PR reviews that were not addressed before merge. | `compute_synthetic_weights` backend algorithm mismatch: Rust path uses Frank-Wolfe (`_rust_synthetic_weights` in `utils.py:1184`); Python fallback uses projected gradient descent (`_compute_synthetic_weights_numpy` in `utils.py:1228`). Both solve the same constrained QP but converge to different simplex vertices on near-degenerate / extreme-scale inputs (e.g. `Y~1e9`, or near-singular `Y'Y`). Unified backend (one algorithm) would close the parity gap surfaced by audit finding #22. Two `@pytest.mark.xfail(strict=True)` tests in `tests/test_rust_backend.py::TestSyntheticWeightsBackendParity` baseline the divergence so we notice when/if the algorithms align. | `utils.py`, `rust/` | follow-up | Medium | | TROP Rust vs Python grid-search divergence on rank-deficient Y: on two near-parallel control units, LOOCV grid-search ATT diverges ~6% between Rust (`trop_global.py:688`) and Python fallback (`trop_global.py:753`). Either grid-winner ties are broken differently or the per-λ solver reaches different stationary points under rank deficiency. Audit finding #23 flagged this surface. `@pytest.mark.xfail(strict=True)` in `tests/test_rust_backend.py::TestTROPRustEdgeCaseParity::test_grid_search_rank_deficient_Y` baselines the gap. | `trop_global.py`, `rust/` | follow-up | Medium | | TROP Rust vs Python bootstrap SE divergence under fixed seed: `seed=42` on a tiny panel produces ~28% bootstrap-SE gap. Root cause: Rust bootstrap uses its own RNG (`rand` crate) while Python uses `numpy.random.default_rng`; same seed value maps to different bytestreams across backends. Audit axis-H (RNG/seed) adjacent. `@pytest.mark.xfail(strict=True)` in `tests/test_rust_backend.py::TestTROPRustEdgeCaseParity::test_bootstrap_seed_reproducibility` baselines the gap. Unifying RNG (threading a numpy-generated seed-sequence into Rust, or porting Python to ChaCha) would close it. | `trop_global.py`, `rust/` | follow-up | Medium | +| `bias_corrected_local_linear`: extend golden parity to `kernel="triangular"` and `kernel="uniform"` (currently epa-only; all three kernels share `kernel_W` and the `lprobust` math, so parity is expected but not separately asserted). | `benchmarks/R/generate_nprobust_lprobust_golden.R`, `tests/test_bias_corrected_lprobust.py` | Phase 1c | Low | +| `bias_corrected_local_linear`: expose `vce in {"hc0", "hc1", "hc2", "hc3"}` on the public wrapper once R parity goldens exist (currently raises `NotImplementedError`). The port-level `lprobust` and `lprobust_res` already support all four; expanding the public surface requires a golden generator for each hc mode and a decision on hc2/hc3 q-fit leverage (R reuses p-fit `hii` for q-fit residuals; whether to match that or stage-match deserves a derivation before the wrapper advertises CCT-2014 conformance). | `diff_diff/local_linear.py::bias_corrected_local_linear`, `benchmarks/R/generate_nprobust_lprobust_golden.R`, `tests/test_bias_corrected_lprobust.py` | Phase 1c | Medium | +| `bias_corrected_local_linear`: support `weights=` once survey-design adaptation lands. nprobust's `lprobust` has no weight argument so there is no parity anchor; derivation needed. | `diff_diff/local_linear.py`, `diff_diff/_nprobust_port.py::lprobust` | Phase 1c | Medium | +| `bias_corrected_local_linear`: support multi-eval grid (`neval > 1`) with cross-covariance (`covgrid=TRUE` branch of `lprobust.R:253-378`). Not needed for HAD but useful for multi-dose diagnostics. | `diff_diff/_nprobust_port.py::lprobust` | Phase 1c | Low | +| Clustered-DGP parity: Phase 1c's DGP 4 uses manual `h=b=0.3` to sidestep an nprobust-internal singleton-cluster bug in `lpbwselect.mse.dpi`'s pilot fits. Once nprobust ships a fix (or we derive one independently), add a clustered-auto-bandwidth parity test. | `benchmarks/R/generate_nprobust_lprobust_golden.R` | Phase 1c | Low | #### Performance diff --git a/benchmarks/R/generate_nprobust_lprobust_golden.R b/benchmarks/R/generate_nprobust_lprobust_golden.R new file mode 100644 index 00000000..5f4ada04 --- /dev/null +++ b/benchmarks/R/generate_nprobust_lprobust_golden.R @@ -0,0 +1,166 @@ +# Generate nprobust lprobust golden values for the Phase 1c parity suite. +# +# This script calls nprobust::lprobust() at a single eval point with +# bwselect="mse-dpi" on five deterministic DGPs and records: +# - tau.cl, tau.bc (point estimates) +# - se.cl, se.rb (standard errors) +# - h, b (bandwidths chosen by the mse-dpi selector) +# - N (observations in the selected kernel window) +# - z = qnorm(1 - alpha/2) +# - ci_low, ci_high = tau.bc +/- z * se.rb +# +# DGPs 1-3 reuse the same seed + shape as benchmarks/R/generate_nprobust_golden.R +# so the selected (h, b) are identical; Phase 1c parity is therefore isolated +# to the point-estimate + variance computation. DGP 4 adds cluster IDs for +# cluster-robust SE parity; DGP 5 shifts the support to test a non-zero +# boundary (Design 1 continuous-near-d_lower). +# +# Usage: +# Rscript benchmarks/R/generate_nprobust_lprobust_golden.R +# +# Requirements: +# nprobust (CRAN), jsonlite +# +# Output: +# benchmarks/data/nprobust_lprobust_golden.json +# +# Phase 1c of the HeterogeneousAdoptionDiD implementation (de Chaisemartin, +# Ciccia, D'Haultfoeuille & Knau 2026, arXiv:2405.04465v6). Python tests at +# tests/test_bias_corrected_lprobust.py and tests/test_nprobust_port.py load +# this JSON and check agreement to tiered tolerances (1e-14 on tau_cl/se_cl, +# 1e-12 on tau_bc/se_rb, 1e-13 on CI bounds; see Phase 1c plan). + +library(nprobust) +library(jsonlite) + +stopifnot(packageVersion("nprobust") == "0.5.0") + +extract_lprobust_single_eval <- function(d, y, eval_point = 0.0, + kernel = "epa", vce = "nn", + cluster = NULL, alpha = 0.05, + h = NULL, b = NULL) { + # If h (and optionally b) are passed, bypass the mse-dpi selector and + # call lprobust() with those bandwidths directly. This is used for + # clustered DGPs where nprobust's internal lpbwselect.mse.dpi hits a + # singleton-cluster shape bug in lprobust.vce during the order-q+1/q+2 + # pilot fits. For unclustered DGPs, h=NULL triggers bwselect="mse-dpi". + if (is.null(h)) { + fit <- lprobust(y = y, x = d, eval = eval_point, + p = 1L, deriv = 0L, kernel = kernel, + bwselect = "mse-dpi", vce = vce, cluster = cluster, + bwcheck = 21L, bwregul = 1, nnmatch = 3L) + } else { + # When b is unspecified, nprobust defaults to b = h / rho with rho=1. + fit <- lprobust(y = y, x = d, eval = eval_point, + p = 1L, deriv = 0L, kernel = kernel, + h = h, b = if (is.null(b)) h else b, + vce = vce, cluster = cluster, + bwcheck = 21L, nnmatch = 3L) + } + est <- fit$Estimate[1, ] + z <- qnorm(1 - alpha / 2) + ci_low <- as.numeric(est["tau.bc"] - z * est["se.rb"]) + ci_high <- as.numeric(est["tau.bc"] + z * est["se.rb"]) + + list( + eval_point = as.numeric(eval_point), + h = as.numeric(est["h"]), + b = as.numeric(est["b"]), + n_used = as.integer(est["N"]), + tau_cl = as.numeric(est["tau.us"]), + tau_bc = as.numeric(est["tau.bc"]), + se_cl = as.numeric(est["se.us"]), + se_rb = as.numeric(est["se.rb"]), + ci_low = ci_low, + ci_high = ci_high, + alpha = as.numeric(alpha), + z = as.numeric(z) + ) +} + +set.seed(20260419) + +# DGP 1: d ~ Uniform(0, 1), y = d + d^2 + N(0, 0.5) +G <- 2000L +d1 <- runif(G, 0, 1) +y1 <- d1 + d1^2 + rnorm(G, 0, 0.5) + +# DGP 2: d ~ Beta(2, 2), y = d + d^2 + N(0, 0.5) (f(0) vanishes at boundary) +d2 <- rbeta(G, 2, 2) +y2 <- d2 + d2^2 + rnorm(G, 0, 0.5) + +# DGP 3: Half-normal d, y = 0.5 * d^2 + N(0, 1) +d3 <- abs(rnorm(G, 0, 1)) +y3 <- 0.5 * d3^2 + rnorm(G, 0, 1) + +# DGP 4: Uniform(0, 1) with 50 clusters of 40 obs (cluster-robust SE parity). +# Fewer, larger clusters avoid an nprobust-internal singleton-cluster shape +# bug in lprobust.vce that fires if a kernel window retains only one obs per +# cluster. 50 clusters x 40 obs => the mse-dpi pilot windows near the +# boundary keep enough obs per cluster to stay well-conditioned. +set.seed(20260420) +G4 <- 2000L +d4 <- runif(G4, 0, 1) +cluster4 <- rep(1:50, each = 40)[1:G4] +# Introduce within-cluster correlation in y via a cluster effect. +cluster_effect <- rnorm(50, 0, 0.3)[cluster4] +y4 <- d4 + d4^2 + cluster_effect + rnorm(G4, 0, 0.3) + +# DGP 5: Uniform(0.2, 1.0) — Design 1 continuous-near-d_lower at +# boundary = d.min() > 0. Different seed to avoid aliasing DGP 1. +set.seed(20260421) +G5 <- 2000L +d5 <- runif(G5, 0.2, 1.0) +y5 <- (d5 - 0.2) + (d5 - 0.2)^2 + rnorm(G5, 0, 0.5) +eval5 <- min(d5) # Design 1 continuous: evaluate at the realized minimum. + +golden <- list( + metadata = list( + nprobust_version = as.character(packageVersion("nprobust")), + nprobust_sha = "36e4e532d2f7d23d4dc6e162575cca79e0927cda", + seeds = list(dgp1 = 20260419L, dgp2 = 20260419L, dgp3 = 20260419L, + dgp4 = 20260420L, dgp5 = 20260421L), + generator = "benchmarks/R/generate_nprobust_lprobust_golden.R", + algorithm = paste("nprobust::lprobust(..., bwselect='mse-dpi') at a single", + "eval point, p=1, deriv=0, kernel='epa', vce='nn'", + "unless noted. The Python wrapper computes its own", + "z_{1-alpha/2} via scipy.stats.norm.ppf inside", + "safe_inference(); R's z is exported here for audit", + "so a reviewer can verify the two critical values", + "agree to machine precision.") + ), + dgp1 = c(list(n = G, d = d1, y = y1, kernel = "epa", vce = "nn", + description = "Uniform(0,1), polynomial m(d) = d + d^2"), + extract_lprobust_single_eval(d1, y1, kernel = "epa", vce = "nn")), + dgp2 = c(list(n = G, d = d2, y = y2, kernel = "epa", vce = "nn", + description = "Beta(2,2) - boundary density vanishes at 0"), + extract_lprobust_single_eval(d2, y2, kernel = "epa", vce = "nn")), + dgp3 = c(list(n = G, d = d3, y = y3, kernel = "epa", vce = "nn", + description = "Half-normal d, quadratic m(d) with unit noise"), + extract_lprobust_single_eval(d3, y3, kernel = "epa", vce = "nn")), + dgp4 = c(list(n = G4, d = d4, y = y4, cluster = cluster4, + kernel = "epa", vce = "nn", + h_manual = 0.3, b_manual = 0.3, + description = paste("Clustered (50 groups of 40) Uniform(0,1);", + "manual h=b=0.3 to sidestep nprobust's", + "singleton-cluster bug in the mse-dpi", + "pilot fits.")), + extract_lprobust_single_eval(d4, y4, kernel = "epa", vce = "nn", + cluster = cluster4, + h = 0.3, b = 0.3)), + dgp5 = c(list(n = G5, d = d5, y = y5, eval_point_override = eval5, + kernel = "epa", vce = "nn", + description = "Uniform(0.2, 1.0), Design 1 boundary = d.min()"), + extract_lprobust_single_eval(d5, y5, eval_point = eval5, + kernel = "epa", vce = "nn")) +) + +out_path <- "benchmarks/data/nprobust_lprobust_golden.json" +dir.create("benchmarks/data", recursive = TRUE, showWarnings = FALSE) +write_json(golden, out_path, auto_unbox = TRUE, pretty = TRUE, digits = 14) +cat("Golden values written to", out_path, "\n") +for (name in c("dgp1", "dgp2", "dgp3", "dgp4", "dgp5")) { + cat(sprintf("%s: tau.bc = %.6f, se.rb = %.6f, h = %.6f, b = %.6f\n", + name, golden[[name]]$tau_bc, golden[[name]]$se_rb, + golden[[name]]$h, golden[[name]]$b)) +} diff --git a/benchmarks/data/nprobust_lprobust_golden.json b/benchmarks/data/nprobust_lprobust_golden.json new file mode 100644 index 00000000..c27db69f --- /dev/null +++ b/benchmarks/data/nprobust_lprobust_golden.json @@ -0,0 +1,119 @@ +{ + "metadata": { + "nprobust_version": "0.5.0", + "nprobust_sha": "36e4e532d2f7d23d4dc6e162575cca79e0927cda", + "seeds": { + "dgp1": 20260419, + "dgp2": 20260419, + "dgp3": 20260419, + "dgp4": 20260420, + "dgp5": 20260421 + }, + "generator": "benchmarks/R/generate_nprobust_lprobust_golden.R", + "algorithm": "nprobust::lprobust(..., bwselect='mse-dpi') at a single eval point, p=1, deriv=0, kernel='epa', vce='nn' unless noted. The Python wrapper computes its own z_{1-alpha/2} via scipy.stats.norm.ppf inside safe_inference(); R's z is exported here for audit so a reviewer can verify the two critical values agree to machine precision." + }, + "dgp1": { + "n": 2000, + "d": [0.346165986265987, 0.209471254376695, 0.349041724810377, 0.716006569564342, 0.898241959745064, 0.243056782288477, 0.994502683868632, 0.72356012603268, 0.617742204340175, 0.184574166312814, 0.360668655950576, 0.0166198713704944, 0.243221291573718, 0.0545476607512683, 0.540974493604153, 0.0504873525351286, 0.261057120747864, 0.391940114321187, 0.814565308624879, 0.108375295298174, 0.13834393187426, 0.512229334330186, 0.0478169047273695, 0.100965828401968, 0.371775630395859, 0.814653061330318, 0.415226160548627, 0.953240251867101, 0.547831779113039, 0.236800524173304, 0.393821754027158, 0.244248000206426, 0.953606148483232, 0.931744179222733, 0.680257683387026, 0.298957533668727, 0.88265565619804, 0.128144388785586, 0.833566912217066, 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-427,6 +429,9 @@ # MSE-optimal bandwidth selector (Phase 1b for HeterogeneousAdoptionDiD) "BandwidthResult", "mse_optimal_bandwidth", + # Bias-corrected local-linear (Phase 1c for HeterogeneousAdoptionDiD) + "BiasCorrectedFit", + "bias_corrected_local_linear", # Datasets "load_card_krueger", "load_castle_doctrine", diff --git a/diff_diff/_nprobust_port.py b/diff_diff/_nprobust_port.py index 5f519ab7..e46fe122 100644 --- a/diff_diff/_nprobust_port.py +++ b/diff_diff/_nprobust_port.py @@ -55,6 +55,7 @@ from __future__ import annotations +import math from dataclasses import dataclass from typing import Optional @@ -66,11 +67,13 @@ "NPROBUST_VERSION", "NPROBUST_SHA", "LprobustBwResult", + "LprobustResult", "kernel_W", "qrXXinv", "lprobust_res", "lprobust_vce", "lprobust_bw", + "lprobust", "lpbwselect_mse_dpi", ] @@ -81,6 +84,33 @@ _VALID_VCE = ("nn", "hc0", "hc1", "hc2", "hc3") +def _cluster_has_missing(cluster: np.ndarray) -> bool: + """Detect missing cluster IDs across float / object / string dtypes. + + nprobust::lpbwselect complete-case-filters (x, y, cluster) before + dispatch. This port deliberately rejects missingness instead so + callers see it rather than silently losing rows. Used by + ``lpbwselect_mse_dpi`` and ``lprobust`` (and the public + ``bias_corrected_local_linear`` wrapper) so all three surfaces + honor the same contract. + """ + if cluster.dtype.kind in ("f", "c"): + return bool(np.any(~np.isfinite(cluster))) + # Object / string / None-containing arrays: treat None and NaN-like + # sentinels as missing. + try: + if bool(np.any([v is None for v in cluster])): + return True + except TypeError: + pass + try: + # np.nan comparisons are False; cast to float and check finiteness. + cluster_f = cluster.astype(np.float64, copy=False) + return bool(np.any(~np.isfinite(cluster_f))) + except (TypeError, ValueError): + return False + + # ============================================================================= # Kernel (W.fun, npfunctions.R:1-7) # ============================================================================= @@ -516,12 +546,18 @@ def lprobust_bw( dups_B = dups[ind1] if dups is not None else None dupsid_B = dupsid[ind1] if dupsid is not None else None if vce in ("hc0", "hc1", "hc2", "hc3"): - predicts_B = R_B1 @ beta_B1 - if vce in ("hc2", "hc3"): - hii_B = np.empty(n_B1, dtype=np.float64) - RW1 = R_B1 * eW1[:, None] - for i in range(n_B1): - hii_B[i] = R_B1[i, :] @ invG_B1 @ RW1[i, :] + # Suppress spurious BLAS FPE warnings (numpy issue #21432 + # pattern); matmul on some platforms (Accelerate / OpenBLAS) + # sets divide/overflow flags on SIMD intermediates even when + # input and output are finite. + with np.errstate(divide="ignore", over="ignore", + invalid="ignore", under="ignore"): + predicts_B = R_B1 @ beta_B1 + if vce in ("hc2", "hc3"): + hii_B = np.empty(n_B1, dtype=np.float64) + RW1 = R_B1 * eW1[:, None] + for i in range(n_B1): + hii_B[i] = R_B1[i, :] @ invG_B1 @ RW1[i, :] res_B = lprobust_res( eX1, eY1, @@ -675,25 +711,9 @@ def lpbwselect_mse_dpi( # before dispatch; this port deliberately rejects instead so # callers see the missingness rather than lose rows silently. # The "reject" vs "filter" choice is documented in the module - # docstring deviations list. - has_missing = False - if cluster.dtype.kind in ("f", "c"): - has_missing = bool(np.any(~np.isfinite(cluster))) - else: - # object / string / None-containing arrays: treat None and - # NaN-like sentinels as missing. - try: - has_missing = bool(np.any([x is None for x in cluster])) - except TypeError: - has_missing = False - if not has_missing: - try: - # np.nan comparisons are False; use pd-style check. - cluster_f = cluster.astype(np.float64, copy=False) - has_missing = bool(np.any(~np.isfinite(cluster_f))) - except (TypeError, ValueError): - pass - if has_missing: + # docstring deviations list. Dtype-agnostic via + # `_cluster_has_missing`. + if _cluster_has_missing(cluster): raise ValueError( "cluster contains missing values (NaN / None). Unlike " "nprobust::lpbwselect which complete-case-filters " @@ -921,3 +941,365 @@ def _fun(H: float) -> float: stage_b=C_b, stage_h=C_h, ) + + +# ============================================================================= +# lprobust single-eval-point path (lprobust.R:177-248) — Phase 1c +# ============================================================================= +# +# Port of the body of nprobust::lprobust's per-eval-point loop iteration from +# lprobust.R (version pinned by NPROBUST_VERSION / NPROBUST_SHA above). The +# single-eval path produces the classical (no bias correction) and Calonico- +# Cattaneo-Titiunik (2014) bias-corrected point estimates plus their naive +# and robust standard errors. The multi-eval grid and the covgrid=TRUE +# cross-covariance branch (lprobust.R:253-378) are intentionally out of scope +# for Phase 1c. +# +# Active-window rule (lprobust.R:181-182, single-eval only): ``ind = ind.b`` +# by default, overwritten to ``ind.h`` only when ``h > b``. This is a +# CONDITIONAL REPLACEMENT, not a union. The union ``ind.h | ind.b`` is only +# used in the covgrid branch. + + +@dataclass +class LprobustResult: + """Single-eval-point result of ``lprobust`` (CCT 2014 bias correction). + + Mirrors the per-eval-point row of nprobust's ``Estimate`` matrix + (lprobust.R:163-164, 248) plus the full intermediate ``(p+1)x(p+1)`` + variance matrices (kept so Phase 2 diagnostics can inspect them). + + Attributes + ---------- + eval_point : float + Evaluation point (``c`` in nprobust's notation; the boundary in HAD). + h, b : float + Main and bias-correction bandwidths as actually used after the + ``bwcheck`` clip (never below the ``bwcheck``-nearest-neighbor floor). + n_used : int + Observations in the SELECTED single kernel window (line 181-182 of + lprobust.R): ``sum(ind.b)`` when ``h <= b`` and ``sum(ind.h)`` when + ``h > b``. With ``rho=1`` default (``h == b``), the two windows + coincide. + tau_cl : float + Classical point estimate ``factorial(deriv) * beta.p[deriv+1]`` + (lprobust.R:226). + tau_bc : float + Bias-corrected point estimate ``factorial(deriv) * + beta.bc[deriv+1]`` (lprobust.R:227), equal to ``mu_hat + M_hat`` in + the Equation 8 notation of de Chaisemartin et al. (2026). + se_cl : float + Naive plug-in standard error ``sqrt(factorial(deriv)^2 * + V.Y.cl[deriv+1, deriv+1])`` (lprobust.R:245). + se_rb : float + Robust standard error under the CCT (2014) bias-corrected + asymptotics ``sqrt(factorial(deriv)^2 * V.Y.bc[deriv+1, deriv+1])`` + (lprobust.R:246). + V_Y_cl, V_Y_bc : np.ndarray, shape (p+1, p+1) + Full classical and robust variance matrices. CI bounds are not + produced here; the public wrapper ``bias_corrected_local_linear`` + adds ``ci_low``/``ci_high`` from ``tau.bc +/- z_{1-alpha/2} * se.rb``. + """ + + eval_point: float + h: float + b: float + n_used: int + tau_cl: float + tau_bc: float + se_cl: float + se_rb: float + V_Y_cl: np.ndarray + V_Y_bc: np.ndarray + + +def lprobust( + y: np.ndarray, + x: np.ndarray, + eval_point: float, + h: float, + b: float, + p: int = 1, + q: Optional[int] = None, + deriv: int = 0, + kernel: str = "epa", + vce: str = "nn", + cluster: Optional[np.ndarray] = None, + nnmatch: int = 3, + bwcheck: Optional[int] = 21, +) -> LprobustResult: + """Local-polynomial point estimate with CCT (2014) bias correction. + + Single-eval port of ``nprobust::lprobust`` (lprobust.R:177-246) from + ``nprobust`` version ``NPROBUST_VERSION`` (SHA ``NPROBUST_SHA``). + Produces both the classical and bias-corrected point estimates along + with their naive and robust standard errors. + + Computation (source-mapped to lprobust.R:177-246): + + w.h = kernel_W((x - eval_point) / h, kernel) / h # line 177 + w.b = kernel_W((x - eval_point) / b, kernel) / b # line 178 + ind.h = w.h > 0; ind.b = w.b > 0 # line 179 + ind = ind.b # line 181 (default) + if h > b: ind = ind.h # line 182 (conditional + # replacement, NOT union) + eN = sum(ind); eY = y[ind]; eX = x[ind] # line 189-191 + W.h = w.h[ind]; W.b = w.b[ind] # line 192-193 + u_h = (eX - eval_point) / h # line 212 + R.q[:, j] = (eX - eval_point)^j, j = 0..q # line 213-214 + R.p = R.q[:, :p+1] # line 215 + L = (R.p * W.h).T @ u_h^(p+1) # line 218 + invG.q = qrXXinv(sqrt(W.b) * R.q) # line 219 + invG.p = qrXXinv(sqrt(W.h) * R.p) # line 220 + e_{p+2}: unit vector with 1 at position p+2 # line 221 (1-indexed) + Q.q = (R.p * W.h) - h^(p+1) * L @ e_{p+2}.T + @ (invG.q @ R.q.T * W.b).T # line 223 (eN, p+1) + beta.p = invG.p @ (R.p * W.h).T @ eY # line 224 + beta.bc = invG.p @ Q.q.T @ eY # line 224 + tau.cl = deriv! * beta.p[deriv+1] # line 226 + tau.bc = deriv! * beta.bc[deriv+1] # line 227 + res.h = lprobust_res(..., j=p+1, vce=vce) # line 239 + res.b = res.h if vce=="nn" else + lprobust_res(..., j=q+1) # line 240-241 + V.Y.cl = invG.p @ lprobust_vce(R.p*W.h, res.h, eC) @ invG.p # line 243 + V.Y.bc = invG.p @ lprobust_vce(Q.q, res.b, eC) @ invG.p # line 244 + se.cl = sqrt(deriv!^2 * V.Y.cl[deriv+1, deriv+1]) # line 245 + se.rb = sqrt(deriv!^2 * V.Y.bc[deriv+1, deriv+1]) # line 246 + + Parameters + ---------- + y, x : np.ndarray, shape (N,) + Outcome and regressor. + eval_point : float + Evaluation point ``c``. + h, b : float + Main and bias-correction bandwidths. Must both be positive. + With nprobust's ``rho=1`` default, the caller passes ``h == b``. + p : int, default=1 + Main polynomial order. + q : int or None, default=None + Bias-correction polynomial order. ``None`` resolves to ``p + 1``. + deriv : int, default=0 + Derivative order to estimate. Must satisfy ``deriv <= p``. + kernel : {"epa", "uni", "tri", "gau"}, default="epa" + vce : {"nn", "hc0", "hc1", "hc2", "hc3"}, default="nn" + cluster : np.ndarray or None + Per-observation cluster IDs for cluster-robust variance. Length + must match ``x``. ``None`` for unclustered. + nnmatch : int, default=3 + Number of nearest neighbors for ``vce="nn"`` residuals. + bwcheck : int or None, default=21 + Floor ``h`` and ``b`` at the distance to the ``bwcheck``-th nearest + neighbor of ``eval_point``. Matches nprobust's default. + + Returns + ------- + LprobustResult + + Raises + ------ + ValueError + On shape mismatch, empty/non-finite inputs, unknown ``kernel``/ + ``vce``, ``deriv > p``, or a rank-deficient design (surfaced from + ``qrXXinv``). + """ + # --- input coercion + validation --- + if kernel not in _VALID_KERNELS: + raise ValueError(f"Unknown kernel {kernel!r}. Expected one of {_VALID_KERNELS}.") + if vce not in _VALID_VCE: + raise ValueError(f"Unknown vce {vce!r}. Expected one of {_VALID_VCE}.") + if p < 0 or deriv < 0 or nnmatch <= 0: + raise ValueError("p, deriv, nnmatch must be nonneg integers; nnmatch > 0.") + if deriv > p: + raise ValueError(f"deriv ({deriv}) cannot exceed p ({p}).") + if q is None: + q = p + 1 + if q < p: + raise ValueError(f"q ({q}) cannot be less than p ({p}).") + if not (np.isfinite(h) and h > 0): + raise ValueError(f"h must be finite and positive; got {h!r}.") + if not (np.isfinite(b) and b > 0): + raise ValueError(f"b must be finite and positive; got {b!r}.") + if not np.isfinite(eval_point): + raise ValueError(f"eval_point must be finite; got {eval_point!r}.") + + x = np.asarray(x, dtype=np.float64).ravel() + y = np.asarray(y, dtype=np.float64).ravel() + if x.shape != y.shape: + raise ValueError(f"x and y must have the same shape; got {x.shape} and {y.shape}.") + N = x.shape[0] + if N == 0: + raise ValueError("x and y must be non-empty.") + if not np.all(np.isfinite(x)): + raise ValueError("x contains non-finite values (NaN or Inf).") + if not np.all(np.isfinite(y)): + raise ValueError("y contains non-finite values (NaN or Inf).") + + if cluster is not None: + cluster = np.asarray(cluster).ravel() + if cluster.shape[0] != N: + raise ValueError( + f"cluster length ({cluster.shape[0]}) does not match x/y ({N})." + ) + # Dtype-agnostic missingness check. Float NaN/Inf, object None, + # and object np.nan all get rejected here (shared with + # `lpbwselect_mse_dpi` via `_cluster_has_missing`) so the + # downstream `lprobust_vce` cluster grouping on `np.unique` + # cannot silently treat a missing sentinel as a real cluster. + if _cluster_has_missing(cluster): + raise ValueError( + "cluster contains missing values (NaN / None). " + "Filter your data before the call or drop missing " + "observations explicitly." + ) + + # --- vce="nn" setup: sort ascending, precompute dups --- + dups: Optional[np.ndarray] = None + dupsid: Optional[np.ndarray] = None + if vce == "nn": + order_x = np.argsort(x, kind="mergesort") # stable, matches R order() + x = x[order_x] + y = y[order_x] + if cluster is not None: + cluster = cluster[order_x] + dups, dupsid = _precompute_nn_duplicates(x) + + # --- bwcheck floor (lprobust.R:169-175) --- + if bwcheck is not None: + if bwcheck < 1 or bwcheck > N: + raise ValueError(f"bwcheck must be in [1, {N}]; got {bwcheck}.") + bw_min = float(np.sort(np.abs(x - eval_point))[bwcheck - 1]) + h = max(h, bw_min) + b = max(b, bw_min) + + # --- kernel weights and active-window selection (lprobust.R:177-182) --- + w_h = kernel_W((x - eval_point) / h, kernel) / h + w_b = kernel_W((x - eval_point) / b, kernel) / b + ind_h = w_h > 0 + ind_b = w_b > 0 + + # Single-eval CONDITIONAL REPLACEMENT: default to the b-window, swap to + # the h-window only when h > b. This is NOT a union; the union form + # appears only in the out-of-scope covgrid branch (lprobust.R:275). + ind = ind_b.copy() + if h > b: + ind = ind_h.copy() + + eN = int(np.sum(ind)) + if eN < (p + 1): + raise ValueError( + f"Active kernel window retains only {eN} observations; need at " + f"least p+1={p+1} for the local-polynomial fit. Widen h/b or " + f"pick a boundary with more distinct values nearby." + ) + + eY = y[ind] + eX = x[ind] + W_h = w_h[ind] + W_b = w_b[ind] + + eC: Optional[np.ndarray] = None + if cluster is not None: + eC = cluster[ind] + + edups: Optional[np.ndarray] = None + edupsid: Optional[np.ndarray] = None + if vce == "nn": + assert dups is not None and dupsid is not None # set above + edups = dups[ind] + edupsid = dupsid[ind] + + # --- design matrices (lprobust.R:212-215) --- + u_h = (eX - eval_point) / h + R_q = np.empty((eN, q + 1), dtype=np.float64) + for j in range(q + 1): + R_q[:, j] = (eX - eval_point) ** j + R_p = R_q[:, : p + 1] + + # --- L vector (lprobust.R:218): L = crossprod(R.p*W.h, u^(p+1)) --- + L = (R_p * W_h[:, None]).T @ (u_h ** (p + 1)) # shape (p+1,) + + # --- Inverses (lprobust.R:219-220) --- + # qrXXinv expects an already-row-scaled design; sqrt(W) * R. + invG_q = qrXXinv(R_q * np.sqrt(W_b)[:, None]) + invG_p = qrXXinv(R_p * np.sqrt(W_h)[:, None]) + + # --- Q.q combined design matrix (lprobust.R:223) --- + # e.p1 has 1 at R-index p+2 (1-indexed) = Python index p+1 (0-indexed). + e_p1 = np.zeros(q + 1, dtype=np.float64) + e_p1[p + 1] = 1.0 + + # R: Q.q <- t(t(R.p*W.h) - h^(p+1)*(L%*%t(e.p1))%*%t(t(invG.q%*%t(R.q))*W.b)) + # Unpacking in Python (see block comment above for derivation). + # BLAS matmul on some platforms (Accelerate / OpenBLAS) can raise + # spurious divide/overflow/invalid FPE warnings on SIMD intermediates + # even when the input and output are finite and bounded. These are + # known benign (numpy issue #21432 pattern); suppress locally. + R_p_W_h = R_p * W_h[:, None] # (eN, p+1) + L_outer = np.outer(L, e_p1) # (p+1, q+1) + with np.errstate(divide="ignore", over="ignore", invalid="ignore", under="ignore"): + C = invG_q @ R_q.T # (q+1, eN) + CW = C * W_b[None, :] # (q+1, eN), column j scaled by W_b[j] + F = (h ** (p + 1)) * (L_outer @ CW) # (p+1, eN) + Q_q = (R_p_W_h.T - F).T # (eN, p+1) + if not np.all(np.isfinite(Q_q)): + raise ValueError( + "Q.q bias-combined design matrix contains non-finite values. " + "This typically signals a degenerate bandwidth pair (h or b " + "near zero) or a rank-deficient window." + ) + + # --- beta.p and beta.bc (lprobust.R:224) --- + beta_p = invG_p @ (R_p_W_h.T @ eY) # (p+1,) + beta_bc = invG_p @ (Q_q.T @ eY) # (p+1,) + + deriv_fact = float(math.factorial(deriv)) + tau_cl = deriv_fact * float(beta_p[deriv]) + tau_bc = deriv_fact * float(beta_bc[deriv]) + + # --- Residuals (lprobust.R:239-241) --- + # hc2/hc3 need the hat-matrix diagonal hii. For vce="nn", hii is unused. + hii: Optional[np.ndarray] = None + predicts_p: Optional[np.ndarray] = None + predicts_q: Optional[np.ndarray] = None + if vce in ("hc0", "hc1", "hc2", "hc3"): + predicts_p = (R_p @ beta_p).reshape(-1, 1) + # R.q @ beta.q where beta.q is the q-polynomial fit. But the R code + # computes beta.q only for hc2/hc3 and reuses it for predicts.q. We + # compute it on demand. + beta_q = invG_q @ ((R_q * W_b[:, None]).T @ eY) # (q+1,) + predicts_q = (R_q @ beta_q).reshape(-1, 1) + if vce in ("hc2", "hc3"): + # hii[j] = R.p[j] @ invG.p @ (R.p * W.h)[j] + # = W.h[j] * R.p[j] @ invG.p @ R.p[j] + RpG = R_p @ invG_p # (eN, p+1) + hii = (np.sum(RpG * R_p, axis=1) * W_h).reshape(-1, 1) + + res_h = lprobust_res(eX, eY, predicts_p if predicts_p is not None else np.zeros((eN, 1)), + hii, vce, nnmatch, edups, edupsid, p + 1) + if vce == "nn": + res_b = res_h + else: + res_b = lprobust_res(eX, eY, predicts_q if predicts_q is not None else np.zeros((eN, 1)), + hii, vce, nnmatch, edups, edupsid, q + 1) + + # --- Variance matrices (lprobust.R:243-244) --- + V_Y_cl = invG_p @ lprobust_vce(R_p_W_h, res_h, eC) @ invG_p + V_Y_bc = invG_p @ lprobust_vce(Q_q, res_b, eC) @ invG_p + + # --- Standard errors (lprobust.R:245-246) --- + se_cl = float(np.sqrt((deriv_fact ** 2) * V_Y_cl[deriv, deriv])) + se_rb = float(np.sqrt((deriv_fact ** 2) * V_Y_bc[deriv, deriv])) + + return LprobustResult( + eval_point=float(eval_point), + h=float(h), + b=float(b), + n_used=eN, + tau_cl=tau_cl, + tau_bc=tau_bc, + se_cl=se_cl, + se_rb=se_rb, + V_Y_cl=V_Y_cl, + V_Y_bc=V_Y_bc, + ) diff --git a/diff_diff/local_linear.py b/diff_diff/local_linear.py index 987d947c..e8286bd2 100644 --- a/diff_diff/local_linear.py +++ b/diff_diff/local_linear.py @@ -20,8 +20,13 @@ ``mse_optimal_bandwidth`` / ``BandwidthResult`` (this module), a thin wrapper over the Calonico-Cattaneo-Farrell (2018) plug-in selector ported in ``diff_diff/_nprobust_port.py``. -- Phase 1c will add the bias-corrected confidence interval per Equation 8 of - de Chaisemartin, Ciccia, D'Haultfoeuille & Knau (2026, arXiv:2405.04465v6). +- Phase 1c ships the bias-corrected local-linear fit + ``bias_corrected_local_linear`` / ``BiasCorrectedFit`` (this module), a + thin wrapper over the Calonico-Cattaneo-Titiunik (2014) robust-bias + correction ported in ``diff_diff/_nprobust_port.py``. This produces + the mu-scale point estimate and CI for Equation 8 of de Chaisemartin, + Ciccia, D'Haultfoeuille & Knau (2026, arXiv:2405.04465v6); Phase 2 + applies the ``(1/G) * sum(D_{g,2})`` beta-scale rescaling. References ---------- @@ -48,6 +53,8 @@ __all__ = [ "BandwidthResult", + "BiasCorrectedFit", + "bias_corrected_local_linear", "epanechnikov_kernel", "triangular_kernel", "uniform_kernel", @@ -492,124 +499,53 @@ class BandwidthResult: } -def mse_optimal_bandwidth( +def _validate_had_inputs( d: np.ndarray, y: np.ndarray, - boundary: float = 0.0, - kernel: str = "epanechnikov", - weights: Optional[np.ndarray] = None, - bwcheck: Optional[int] = 21, - bwregul: float = 1.0, - return_diagnostics: bool = False, -): - """MSE-optimal bandwidth for local-linear regression at a boundary. - - Port of ``nprobust::lpbwselect(bwselect="mse-dpi")`` (R package - ``nprobust`` 0.5.0, SHA ``36e4e53``). Implements the Calonico, - Cattaneo, and Farrell (2018, JASA 113(522)) direct-plug-in DPI - bandwidth selector for the local-linear boundary estimator used in - Design 1' of de Chaisemartin et al. (2026) (HAD). - - The three-stage algorithm produces five bandwidths (``c_bw``, - ``bw_mp2``, ``bw_mp3``, ``b_mse``, ``h_mse``); see - ``BandwidthResult`` for full diagnostics. - - **Public API scope (Phase 1b of HAD).** This wrapper is intentionally - restricted to the HAD configuration: ``p=1`` (local-linear), - ``deriv=0`` (mean regression), ``interior=False`` (boundary eval - point), ``vce="nn"`` (nearest-neighbor variance), and ``nnmatch=3`` - are hard-coded. The underlying ``diff_diff._nprobust_port`` supports - additional ``vce`` modes (``hc0`` / ``hc1`` / ``hc2`` / ``hc3``), - interior evaluation, and higher polynomial orders, but those paths - are NOT parity-tested against ``nprobust`` and are deferred to Phase - 1c / Phase 2. Do not rely on this public wrapper for anything - outside HAD Phase 1b; use the port directly if you need the broader - surface and accept that parity has not been separately verified. + boundary: float, +) -> tuple[np.ndarray, np.ndarray]: + """HAD-scope input validation shared across Phase 1b and Phase 1c. + + Coerces ``d`` and ``y`` to 1-D ``float64`` arrays and enforces the + HAD input contract used by both ``mse_optimal_bandwidth`` (Phase 1b) + and ``bias_corrected_local_linear`` (Phase 1c). Each caller handles + its own ``weights=`` rejection upfront because the phase name and + message differ; every other rule lives here. + + Rules (matching Phase 1b's original inline checks verbatim): + + - ``d`` and ``y`` must be shape-matched, non-empty, and finite. + - ``boundary`` must be finite. + - Doses are nonnegative (HAD support ``D_{g,2} >= 0``). + - ``boundary`` is approximately ``0`` (Design 1') or approximately + ``d.min()`` (Design 1 continuous-near-d_lower); other boundaries + raise so off-support calls do not silently target a different + estimand. + - Mass-point designs at ``d.min() > 0`` with modal-min fraction + ``> 2%`` raise ``NotImplementedError`` pointing to Phase 2's 2SLS + path, per the paper's Section 3.2.4. + - Design 1' plausibility heuristic: when ``boundary ~ 0``, require + ``d.min() <= 0.05 * median(|d|)``; samples above that ratio are + redirected to ``boundary=float(d.min())``. Parameters ---------- - d : np.ndarray, shape (G,) - Regressor values (the dose ``D_{g,2}`` in HAD). - y : np.ndarray, shape (G,) - Outcome values (the first-difference ``Delta Y_g`` in HAD). - boundary : float, default=0.0 - Evaluation point ``d_0``. The Phase 1b wrapper accepts only - two values (within float tolerance): ``boundary = 0`` for - Design 1' or ``boundary = float(d.min())`` for Design 1 - continuous-near-``d_lower``. Use the sample minimum - ``d.min()`` (not a known theoretical lower bound of the - support), because the downstream selector operates on the - realized data. Any other value -- including - ``boundary < d.min()``, interior points, or - ``boundary > d.min()`` -- raises ``ValueError``. - kernel : str, default="epanechnikov" - One of ``"epanechnikov"``, ``"triangular"``, ``"uniform"``. - weights : np.ndarray or None, default=None - Not supported in Phase 1b (raises ``NotImplementedError``). - ``nprobust::lpbwselect`` has no weight argument and thus no - parity anchor. Weighted-data support is queued for Phase 2+. - bwcheck : int or None, default=21 - If set, clip ``c_bw`` (and all downstream bandwidths) below the - distance to the ``bwcheck``-th nearest neighbor of ``boundary``. - Matches ``nprobust::lpbwselect`` default. - bwregul : float, default=1.0 - Bias-regularization scale used in Stage 3. ``bwregul=0`` disables - the regularization term; ``bwregul=1`` matches ``nprobust`` - default. - return_diagnostics : bool, default=False - When ``False`` (default) return the final bandwidth as a - ``float``. When ``True`` return a ``BandwidthResult`` with all - five stage bandwidths plus per-stage ``(V, B1, B2, R)`` - diagnostics. + d, y : np.ndarray + Regressor (dose) and outcome. + boundary : float + Evaluation point. Returns ------- - float or BandwidthResult - The MSE-optimal bandwidth ``h*``, or (if - ``return_diagnostics=True``) a ``BandwidthResult`` dataclass. + tuple[np.ndarray, np.ndarray] + Coerced ``(d, y)`` as 1-D float64 arrays. Raises ------ - ValueError - Raised on: shape mismatch between ``d`` and ``y``; non-finite - values in ``d``, ``y``, or ``boundary``; unknown ``kernel`` - name; ``bwcheck`` outside ``[1, len(d)]``; ``boundary`` that - is not approximately 0 or approximately ``d.min()`` (the only - two supported HAD estimands in Phase 1b); or a rank-deficient - / under-determined pilot fit inside the DPI port (surfaced - from ``qrXXinv`` or the per-stage count guards in - ``lprobust_bw``). - NotImplementedError - Raised on: ``weights=`` passed (no nprobust parity anchor); - detected Design 1 mass-point design (``d.min() > 0`` and - modal fraction at ``d.min()`` exceeds 2%, per the paper's - Section 3.2.4 redirection to the 2SLS sample-average - estimator, queued for Phase 2). - - Notes - ----- - The port parity-tests at 1% relative error against R - ``nprobust::lpbwselect(bwselect="mse-dpi", vce="nn")`` on three - deterministic DGPs (see - ``benchmarks/R/generate_nprobust_golden.R``). In practice the - agreement is at machine precision (0.0000% on the golden tests); - the 1% tolerance is the hard gate before a deviation is considered - a regression. + ValueError, NotImplementedError """ - if weights is not None: - raise NotImplementedError( - "weights= is not supported in Phase 1b of the MSE-optimal " - "bandwidth selector. nprobust::lpbwselect has no weight " - "argument, so there is no parity anchor. Weighted-data " - "support is queued for Phase 2+ (survey-design adaptation)." - ) - - if kernel not in _KERNEL_NAME_TO_NPROBUST: - raise ValueError( - f"Unknown kernel {kernel!r}. Expected one of " - f"{sorted(_KERNEL_NAME_TO_NPROBUST.keys())}." - ) - nprobust_kernel = _KERNEL_NAME_TO_NPROBUST[kernel] + if not np.isfinite(boundary): + raise ValueError(f"boundary must be finite; got {boundary}") d = np.asarray(d, dtype=np.float64).ravel() y = np.asarray(y, dtype=np.float64).ravel() @@ -624,8 +560,6 @@ def mse_optimal_bandwidth( raise ValueError("d contains non-finite values (NaN or Inf)") if not np.all(np.isfinite(y)): raise ValueError("y contains non-finite values (NaN or Inf)") - if not np.isfinite(boundary): - raise ValueError(f"boundary must be finite; got {boundary}") # HAD support restriction: de Chaisemartin et al. (2026) Assumption # (dose definition in Section 2) treats ``D_{g,2}`` as the period-2 @@ -733,6 +667,130 @@ def mse_optimal_bandwidth( f"would be closer to zero relative to the data scale)." ) + return d, y + + +def mse_optimal_bandwidth( + d: np.ndarray, + y: np.ndarray, + boundary: float = 0.0, + kernel: str = "epanechnikov", + weights: Optional[np.ndarray] = None, + bwcheck: Optional[int] = 21, + bwregul: float = 1.0, + return_diagnostics: bool = False, +): + """MSE-optimal bandwidth for local-linear regression at a boundary. + + Port of ``nprobust::lpbwselect(bwselect="mse-dpi")`` (R package + ``nprobust`` 0.5.0, SHA ``36e4e53``). Implements the Calonico, + Cattaneo, and Farrell (2018, JASA 113(522)) direct-plug-in DPI + bandwidth selector for the local-linear boundary estimator used in + Design 1' of de Chaisemartin et al. (2026) (HAD). + + The three-stage algorithm produces five bandwidths (``c_bw``, + ``bw_mp2``, ``bw_mp3``, ``b_mse``, ``h_mse``); see + ``BandwidthResult`` for full diagnostics. + + **Public API scope (Phase 1b of HAD).** This wrapper is intentionally + restricted to the HAD configuration: ``p=1`` (local-linear), + ``deriv=0`` (mean regression), ``interior=False`` (boundary eval + point), ``vce="nn"`` (nearest-neighbor variance), and ``nnmatch=3`` + are hard-coded. The underlying ``diff_diff._nprobust_port`` supports + additional ``vce`` modes (``hc0`` / ``hc1`` / ``hc2`` / ``hc3``), + interior evaluation, and higher polynomial orders, but those paths + are NOT parity-tested against ``nprobust`` and are deferred to Phase + 1c / Phase 2. Do not rely on this public wrapper for anything + outside HAD Phase 1b; use the port directly if you need the broader + surface and accept that parity has not been separately verified. + + Parameters + ---------- + d : np.ndarray, shape (G,) + Regressor values (the dose ``D_{g,2}`` in HAD). + y : np.ndarray, shape (G,) + Outcome values (the first-difference ``Delta Y_g`` in HAD). + boundary : float, default=0.0 + Evaluation point ``d_0``. The Phase 1b wrapper accepts only + two values (within float tolerance): ``boundary = 0`` for + Design 1' or ``boundary = float(d.min())`` for Design 1 + continuous-near-``d_lower``. Use the sample minimum + ``d.min()`` (not a known theoretical lower bound of the + support), because the downstream selector operates on the + realized data. Any other value -- including + ``boundary < d.min()``, interior points, or + ``boundary > d.min()`` -- raises ``ValueError``. + kernel : str, default="epanechnikov" + One of ``"epanechnikov"``, ``"triangular"``, ``"uniform"``. + weights : np.ndarray or None, default=None + Not supported in Phase 1b (raises ``NotImplementedError``). + ``nprobust::lpbwselect`` has no weight argument and thus no + parity anchor. Weighted-data support is queued for Phase 2+. + bwcheck : int or None, default=21 + If set, clip ``c_bw`` (and all downstream bandwidths) below the + distance to the ``bwcheck``-th nearest neighbor of ``boundary``. + Matches ``nprobust::lpbwselect`` default. + bwregul : float, default=1.0 + Bias-regularization scale used in Stage 3. ``bwregul=0`` disables + the regularization term; ``bwregul=1`` matches ``nprobust`` + default. + return_diagnostics : bool, default=False + When ``False`` (default) return the final bandwidth as a + ``float``. When ``True`` return a ``BandwidthResult`` with all + five stage bandwidths plus per-stage ``(V, B1, B2, R)`` + diagnostics. + + Returns + ------- + float or BandwidthResult + The MSE-optimal bandwidth ``h*``, or (if + ``return_diagnostics=True``) a ``BandwidthResult`` dataclass. + + Raises + ------ + ValueError + Raised on: shape mismatch between ``d`` and ``y``; non-finite + values in ``d``, ``y``, or ``boundary``; unknown ``kernel`` + name; ``bwcheck`` outside ``[1, len(d)]``; ``boundary`` that + is not approximately 0 or approximately ``d.min()`` (the only + two supported HAD estimands in Phase 1b); or a rank-deficient + / under-determined pilot fit inside the DPI port (surfaced + from ``qrXXinv`` or the per-stage count guards in + ``lprobust_bw``). + NotImplementedError + Raised on: ``weights=`` passed (no nprobust parity anchor); + detected Design 1 mass-point design (``d.min() > 0`` and + modal fraction at ``d.min()`` exceeds 2%, per the paper's + Section 3.2.4 redirection to the 2SLS sample-average + estimator, queued for Phase 2). + + Notes + ----- + The port parity-tests at 1% relative error against R + ``nprobust::lpbwselect(bwselect="mse-dpi", vce="nn")`` on three + deterministic DGPs (see + ``benchmarks/R/generate_nprobust_golden.R``). In practice the + agreement is at machine precision (0.0000% on the golden tests); + the 1% tolerance is the hard gate before a deviation is considered + a regression. + """ + if weights is not None: + raise NotImplementedError( + "weights= is not supported in Phase 1b of the MSE-optimal " + "bandwidth selector. nprobust::lpbwselect has no weight " + "argument, so there is no parity anchor. Weighted-data " + "support is queued for Phase 2+ (survey-design adaptation)." + ) + + if kernel not in _KERNEL_NAME_TO_NPROBUST: + raise ValueError( + f"Unknown kernel {kernel!r}. Expected one of " + f"{sorted(_KERNEL_NAME_TO_NPROBUST.keys())}." + ) + nprobust_kernel = _KERNEL_NAME_TO_NPROBUST[kernel] + + d, y = _validate_had_inputs(d, y, boundary) + # Defer heavy import to call time to avoid import-cycle risk. from diff_diff._nprobust_port import lpbwselect_mse_dpi @@ -781,3 +839,408 @@ def mse_optimal_bandwidth( kernel=kernel, boundary=float(boundary), ) + + +# ============================================================================= +# Bias-corrected local-linear fit (Phase 1c) +# ============================================================================= +# +# Public wrapper around diff_diff._nprobust_port.lprobust. The port is a +# faithful Python translation of the single-eval-point path of +# nprobust::lprobust (R package nprobust 0.5.0, SHA 36e4e53) implementing +# Calonico, Cattaneo, and Titiunik (2014) robust bias correction. +# +# This wrapper produces the mu-scale quantities of Equation 8 in de +# Chaisemartin, Ciccia, D'Haultfoeuille, and Knau (2026): a classical and +# bias-corrected point estimate plus naive and robust standard errors and +# the bias-corrected confidence interval [tau.bc +/- z_{1-alpha/2} * se.rb]. +# The beta-scale rescaling in Equation 8 (divide by ``(1/G) * sum(D_{g,2})``) +# is applied by Phase 2's ``HeterogeneousAdoptionDiD.fit()``, not here. + + +@dataclass +class BiasCorrectedFit: + """Bias-corrected local-linear fit at a boundary (Phase 1c). + + Output of :func:`bias_corrected_local_linear`. Produces the mu-scale + quantities needed by Equation 8 of de Chaisemartin, Ciccia, + D'Haultfoeuille, and Knau (2026). Phase 2's ``HeterogeneousAdoptionDiD`` + class applies the beta-scale ``(1/G) * sum(D_{g,2})`` rescaling. + + Attributes + ---------- + estimate_classical : float + Classical point estimate ``tau.cl`` from ``nprobust::lprobust`` + (local-linear boundary intercept at ``h``; no bias correction). + estimate_bias_corrected : float + Bias-corrected point estimate ``tau.bc = mu_hat + M_hat`` from the + Calonico-Cattaneo-Titiunik (2014) combined design-matrix statistic. + se_classical : float + Naive plug-in standard error. + se_robust : float + Robust standard error accounting for the additional variability + introduced by the bias-correction term (CCT 2014). + ci_low, ci_high : float + Endpoints of the bias-corrected CI: ``tau.bc +/- z_{1-alpha/2} * + se.rb``. + alpha : float + CI level (``0.05`` gives a 95% CI). + h, b : float + Main and bias-correction bandwidths actually used (post-``bwcheck`` + floor). + bandwidth_source : {"auto", "user"} + ``"auto"`` when the wrapper called the Phase 1b DPI selector + (``_nprobust_port.lpbwselect_mse_dpi``) internally with the + caller's ``cluster`` / ``vce`` / ``nnmatch``; ``"user"`` when the + caller passed explicit bandwidths. Auto mode then enforces + nprobust's ``rho=1`` default by setting ``b = h``; the + selector's distinct ``b_mse`` is surfaced via + ``bandwidth_diagnostics`` but not applied. + bandwidth_diagnostics : BandwidthResult or None + Full Phase 1b selector output when ``bandwidth_source == "auto"``; + ``None`` when the user supplied bandwidths (to avoid a redundant + selector call). + n_used : int + Observations retained in the active kernel window (``sum(ind.b)`` + when ``h <= b`` and ``sum(ind.h)`` when ``h > b``; with the + ``rho=1`` default the two coincide). + n_total : int + Total observations passed in (before kernel filtering). + kernel : str + Kernel name as supplied by the caller. + boundary : float + Evaluation point ``c``. + + Notes + ----- + ``p=1``, ``q=2``, ``deriv=0`` are hard-coded for HAD Phase 1c and are + not exposed as fields. Phase 2 may surface them on the estimator-level + result class if a use case materializes. + """ + + estimate_classical: float + estimate_bias_corrected: float + se_classical: float + se_robust: float + ci_low: float + ci_high: float + alpha: float + h: float + b: float + bandwidth_source: str + bandwidth_diagnostics: Optional[BandwidthResult] + n_used: int + n_total: int + kernel: str + boundary: float + + +def bias_corrected_local_linear( + d: np.ndarray, + y: np.ndarray, + boundary: float = 0.0, + kernel: str = "epanechnikov", + h: Optional[float] = None, + b: Optional[float] = None, + alpha: float = 0.05, + vce: str = "nn", + cluster: Optional[np.ndarray] = None, + nnmatch: int = 3, + weights: Optional[np.ndarray] = None, +) -> BiasCorrectedFit: + """Bias-corrected local-linear fit with robust CI at a boundary. + + Ports the single-eval-point path of ``nprobust::lprobust`` (Calonico, + Cattaneo, and Titiunik 2014) and produces the mu-scale outputs of + Equation 8 in de Chaisemartin, Ciccia, D'Haultfoeuille, and Knau + (2026). Phase 2's ``HeterogeneousAdoptionDiD`` class applies the + beta-scale rescaling to return the final estimand. + + **Public API scope (Phase 1c of HAD).** Hard-coded for the HAD + configuration: ``p=1`` (local-linear), ``q=2`` (bias-correction + order), ``deriv=0`` (level), ``interior=False`` (boundary eval + point), ``bwcheck=21``, ``bwregul=1``, and ``vce="nn"`` (the only + variance mode golden-parity-tested against R in this phase). The + underlying ``diff_diff._nprobust_port.lprobust`` accepts the broader + surface (hc0/hc1/hc2/hc3, higher ``p``, interior eval), but those + paths are not separately parity-tested and remain private until + Phase 2+ ships dedicated goldens. + + Parameters + ---------- + d : np.ndarray, shape (G,) + Regressor (dose ``D_{g,2}`` in HAD). + y : np.ndarray, shape (G,) + Outcome (first-difference ``Delta Y_g`` in HAD). + boundary : float, default=0.0 + Evaluation point ``d_0``. Accepts only ``boundary ~ 0`` (Design 1') + or ``boundary ~ float(d.min())`` (Design 1 continuous-near-d_lower); + see Phase 1b's input contract. + kernel : {"epanechnikov", "triangular", "uniform"}, default="epanechnikov" + h : float or None, default=None + Main bandwidth. ``None`` auto-selects ``h`` by calling + ``diff_diff._nprobust_port.lpbwselect_mse_dpi`` directly with the + supplied ``cluster``, ``vce``, and ``nnmatch`` so the selector + and the final fit use the same estimator. ``b`` is then set to + ``h`` per nprobust's ``rho=1`` default; the selector's distinct + ``b_mse`` is surfaced through + ``BiasCorrectedFit.bandwidth_diagnostics`` for inspection but + not applied. If ``h`` is provided and ``b`` is ``None``, + ``b = h`` likewise. + b : float or None, default=None + Bias-correction bandwidth. Pairs with ``h`` (see above). ``b`` + provided without ``h`` raises ``ValueError``. + alpha : float, default=0.05 + CI level; ``0.05`` gives a 95% CI. Must be in ``(0, 1)``. + vce : {"nn"}, default="nn" + Variance-estimation method. Only ``"nn"`` is supported in + Phase 1c; hc0/hc1/hc2/hc3 are queued for Phase 2+ pending + dedicated R parity goldens. Passing anything else raises + ``NotImplementedError``. + cluster : np.ndarray or None + Per-observation cluster IDs for cluster-robust variance. Missing + (NaN) cluster IDs raise ``ValueError`` rather than silently + dropping rows. + nnmatch : int, default=3 + Number of nearest neighbors for ``vce="nn"`` residuals. + weights : np.ndarray or None, default=None + Not supported in Phase 1c (raises ``NotImplementedError``). + + Returns + ------- + BiasCorrectedFit + + Raises + ------ + ValueError + Shape mismatch, non-finite inputs, off-support boundary, negative + doses, ``alpha`` outside ``(0, 1)``, unknown ``kernel``, + NaN / None cluster IDs, ``b`` supplied without ``h``, or a + rank-deficient window. + NotImplementedError + ``weights=`` passed; ``vce != "nn"`` (hc0/hc1/hc2/hc3 deferred + to Phase 2+ pending dedicated R parity goldens); a Design 1 + mass-point sample (redirects to Phase 2's 2SLS sample-average + path per the paper's Section 3.2.4). + + Notes + ----- + Parity against ``nprobust::lprobust(..., bwselect="mse-dpi")`` is + asserted at ``atol=1e-12`` on ``tau_cl``, ``tau_bc``, ``se_cl``, + ``se_rb``, ``ci_low``, and ``ci_high`` across the three unclustered + golden DGPs; DGP 1 and DGP 3 typically land closer to ``1e-13``. + The Python wrapper computes its own ``z_{1-alpha/2}`` via + ``scipy.stats.norm.ppf`` inside ``safe_inference()``; R's ``qnorm`` + value is stored in the golden JSON for audit, and the parity harness + compares Python's CI bounds to R's pre-computed CI bounds, so any + residual drift is purely the floating-point arithmetic in + ``tau.bc +/- z * se.rb``, not a critical-value disagreement. + Clustered DGP 4 achieves bit-parity (``atol=1e-14``) when cluster + IDs are in first-appearance order; otherwise BLAS reduction + ordering can drift to ``atol=1e-10``. + """ + if weights is not None: + raise NotImplementedError( + "weights= is not supported in Phase 1c of the bias-corrected " + "local-linear estimator. nprobust::lprobust has no weight " + "argument, so there is no parity anchor. Weighted-data " + "support is queued for Phase 2+ (survey-design adaptation)." + ) + + if kernel not in _KERNEL_NAME_TO_NPROBUST: + raise ValueError( + f"Unknown kernel {kernel!r}. Expected one of " + f"{sorted(_KERNEL_NAME_TO_NPROBUST.keys())}." + ) + nprobust_kernel = _KERNEL_NAME_TO_NPROBUST[kernel] + + if not (0.0 < alpha < 1.0): + raise ValueError(f"alpha must be in (0, 1); got {alpha!r}.") + + # Phase 1c public-wrapper vce restriction. + # Only ``vce="nn"`` is golden-tested against R in Phase 1c. Exposing + # hc0/hc1/hc2/hc3 on the public surface would ship non-parity-verified + # inference -- and nprobust's internal hc2/hc3 residual path reuses + # the p-fit hat-matrix leverage for the q-fit residuals (lprobust.R + # :229-241), a detail that would need its own R parity anchor before + # the Python port can advertise it as CCT-2014-compliant. Defer the + # hc-mode public surface to Phase 2+ with dedicated goldens. The + # port-level ``diff_diff._nprobust_port.lprobust`` still accepts + # hc0..hc3 for callers who need the broader surface and accept that + # the hc-mode behavior has not been separately parity-tested. + if vce != "nn": + raise NotImplementedError( + f"vce={vce!r} is not supported on bias_corrected_local_linear " + "in Phase 1c. Only vce='nn' is golden-tested against R " + "nprobust::lprobust in this phase; hc0/hc1/hc2/hc3 are queued " + "for Phase 2+ pending dedicated R parity goldens. If you need " + "the hc-mode port path for exploratory use, call " + "diff_diff._nprobust_port.lprobust directly and accept that " + "those paths are not separately parity-tested." + ) + + # HAD-scope input validation (shared with Phase 1b via _validate_had_inputs). + d, y = _validate_had_inputs(d, y, boundary) + n_total = int(d.shape[0]) + + # Reject missing cluster IDs up front (Phase 1b convention). Delegates + # to the dtype-agnostic `_cluster_has_missing` helper in the port so + # wrapper, port-level `lprobust`, and `lpbwselect_mse_dpi` all enforce + # the same missing-sentinel contract across float / object / string + # dtypes (CI review PR #340 P1 follow-up). + cluster_arr: Optional[np.ndarray] = None + if cluster is not None: + from diff_diff._nprobust_port import _cluster_has_missing + + cluster_arr = np.asarray(cluster).ravel() + if cluster_arr.shape[0] != n_total: + raise ValueError( + f"cluster length ({cluster_arr.shape[0]}) does not match " + f"d/y ({n_total})." + ) + if _cluster_has_missing(cluster_arr): + raise ValueError( + "cluster contains missing values (NaN / None). Filter " + "your data before the call or drop missing observations " + "explicitly." + ) + + # --- Resolve (h, b) --- + # nprobust's lprobust() with the default rho=1 sets b = h / rho = h + # whenever h is unspecified (R lprobust.R:121-124 and 139: even though + # lpbwselect returns a distinct b_mse, `if (rho>0) b <- h/rho` discards + # it). Auto mode here matches that behavior to preserve bit-parity. The + # distinct b_mse from Phase 1b's selector is still surfaced via + # bandwidth_diagnostics.b_mse for callers that want to inspect or + # override. The paper (de Chaisemartin et al. 2026) likewise uses a + # single h*_G throughout Equation 8. + # + # In auto mode, cluster / vce / nnmatch are forwarded to + # ``lpbwselect_mse_dpi`` so bandwidth selection reflects the same + # estimator the final ``lprobust`` call will use. Calling + # ``mse_optimal_bandwidth`` (the public wrapper) instead would hard-code + # ``cluster=None, vce="nn", nnmatch=3`` and silently mismatch the + # downstream fit — a methodology bug (CI review PR #340 P1). + bw_source: str + bw_diag: Optional[BandwidthResult] = None + if h is None and b is None: + # Defer heavy import to call time to avoid import-cycle risk. + from diff_diff._nprobust_port import lpbwselect_mse_dpi + + stages = lpbwselect_mse_dpi( + y=y, + x=d, + cluster=cluster_arr, + eval_point=float(boundary), + p=1, + q=2, + deriv=0, + kernel=nprobust_kernel, + bwcheck=21, + bwregul=1.0, + vce=vce, + nnmatch=nnmatch, + interior=False, + ) + bw_diag = BandwidthResult( + h_mse=stages.h_mse_dpi, + b_mse=stages.b_mse_dpi, + c_bw=stages.c_bw, + bw_mp2=stages.bw_mp2, + bw_mp3=stages.bw_mp3, + stage_d1_V=stages.stage_d1.V, + stage_d1_B1=stages.stage_d1.B1, + stage_d1_B2=stages.stage_d1.B2, + stage_d1_R=stages.stage_d1.R, + stage_d2_V=stages.stage_d2.V, + stage_d2_B1=stages.stage_d2.B1, + stage_d2_B2=stages.stage_d2.B2, + stage_d2_R=stages.stage_d2.R, + stage_b_V=stages.stage_b.V, + stage_b_B1=stages.stage_b.B1, + stage_b_B2=stages.stage_b.B2, + stage_b_R=stages.stage_b.R, + stage_h_V=stages.stage_h.V, + stage_h_B1=stages.stage_h.B1, + stage_h_B2=stages.stage_h.B2, + stage_h_R=stages.stage_h.R, + n=int(d.shape[0]), + kernel=kernel, + boundary=float(boundary), + ) + h_val = float(bw_diag.h_mse) + b_val = h_val # rho=1 default to match nprobust + bw_source = "auto" + elif h is not None: + h_val = float(h) + if b is None: + b_val = h_val # nprobust rho=1 default + else: + b_val = float(b) + if not (np.isfinite(h_val) and h_val > 0): + raise ValueError(f"h must be finite and positive; got {h!r}.") + if not (np.isfinite(b_val) and b_val > 0): + raise ValueError(f"b must be finite and positive; got {b!r}.") + bw_source = "user" + else: + # h is None but b is not None: ambiguous. + raise ValueError( + "b provided without h; pass both bandwidths explicitly or " + "leave both as None to auto-select via mse_optimal_bandwidth." + ) + + # --- Call the port --- + # Defer heavy import to call time to avoid import-cycle risk. + from diff_diff._nprobust_port import lprobust + + result = lprobust( + y=y, + x=d, + eval_point=float(boundary), + h=h_val, + b=b_val, + p=1, + q=2, + deriv=0, + kernel=nprobust_kernel, + vce=vce, + cluster=cluster_arr, + nnmatch=nnmatch, + bwcheck=21, + ) + + # --- Bias-corrected CI via safe_inference (NaN-safe gate) --- + # When se_robust is zero, negative, or non-finite (e.g., exact-fit + # cases where the residual vector collapses), ALL inference fields — + # including the CI — must return NaN. This enforces the repo-wide + # inference contract (CLAUDE.md Key Design Pattern #6; CI review + # PR #340 P0) rather than returning a misleading zero-width or + # infinite CI. safe_inference computes the critical value z_{1-α/2} + # via scipy.stats.norm.ppf; the parity tests compare Python's + # scipy-computed ci_low/ci_high to R's qnorm-computed ci_low/ci_high + # stored in the golden JSON. The golden JSON also exports R's raw + # `z` value for audit/reference so a reviewer can verify the two + # critical values agree to machine precision. + from diff_diff.utils import safe_inference + + _, _, (ci_low, ci_high) = safe_inference( + result.tau_bc, result.se_rb, alpha=float(alpha) + ) + + return BiasCorrectedFit( + estimate_classical=result.tau_cl, + estimate_bias_corrected=result.tau_bc, + se_classical=result.se_cl, + se_robust=result.se_rb, + ci_low=ci_low, + ci_high=ci_high, + alpha=float(alpha), + h=result.h, + b=result.b, + bandwidth_source=bw_source, + bandwidth_diagnostics=bw_diag, + n_used=result.n_used, + n_total=n_total, + kernel=kernel, + boundary=float(boundary), + ) diff --git a/docs/methodology/REGISTRY.md b/docs/methodology/REGISTRY.md index 9affc045..5847b819 100644 --- a/docs/methodology/REGISTRY.md +++ b/docs/methodology/REGISTRY.md @@ -2313,8 +2313,8 @@ Shipped as `did_had_pretest_workflow()` and surfaced via `practitioner_next_step - **Note (deviation from the fully-symmetric enum):** `MultiPeriodDiD(cluster=..., vcov_type="hc2_bm")` is intentionally **not supported** and raises `NotImplementedError`. The scalar-coefficient `DifferenceInDifferences` path handles the cluster + CR2 Bell-McCaffrey combination (`_compute_cr2_bm` returns a per-coefficient Satterthwaite DOF that is valid for the single-ATT contrast), but `MultiPeriodDiD` also reports a post-period-average ATT constructed as a *contrast* of the event-study coefficients. The cluster-aware CR2 BM DOF for that contrast (i.e., the Pustejovsky-Tipton 2018 per-cluster adjustment matrices applied to an arbitrary aggregation contrast) is not yet implemented. Pairing CR2 cluster-robust SEs with the one-way Imbens-Kolesar (2016) contrast DOF would be a broken hybrid, so the combination fails fast with a clear workaround message (drop the cluster for one-way HC2+BM, or use `vcov_type="hc1"` with cluster for CR1 Liang-Zeger). Tracked in `TODO.md` under Methodology/Correctness. Applies only to `MultiPeriodDiD`; `DifferenceInDifferences(cluster=..., vcov_type="hc2_bm")` works. - [x] Phase 1a: `clubSandwich::vcovCR(..., type="CR2")` parity harness committed: R script at `benchmarks/R/generate_clubsandwich_golden.R` plus a regression-anchor JSON at `benchmarks/data/clubsandwich_cr2_golden.json`. **Note:** the committed JSON currently has `"source": "python_self_reference"` and pins numerical stability only; authoritative R-produced values are generated by running the R script, which the TODO.md row under Methodology/Correctness tracks. The parity test at `tests/test_linalg_hc2_bm.py::TestCR2BMCluster::test_cr2_parity_with_golden` runs at 1e-6 tolerance (Phase 1a plan commits 6-digit parity once R regen completes). - [x] Phase 1b: Calonico-Cattaneo-Farrell (2018) MSE-optimal bandwidth selector. In-house port of `nprobust::lpbwselect(bwselect="mse-dpi")` (nprobust 0.5.0, SHA `36e4e53`) as `diff_diff.mse_optimal_bandwidth` and `BandwidthResult`, backed by the private `diff_diff._nprobust_port` module (`kernel_W`, `lprobust_bw`, `lpbwselect_mse_dpi`). Three-stage DPI with four `lprobust.bw` calls at orders `q+1`, `q+2`, `q`, `p`. Python matches R to `0.0000%` relative error (i.e., bit-parity within float64 precision, ~8-13 digits agreement) on all five stage bandwidths (`c_bw`, `bw_mp2`, `bw_mp3`, `b_mse`, `h_mse`) across three deterministic DGPs (uniform, Beta(2,2), half-normal) via `benchmarks/R/generate_nprobust_golden.R` → `benchmarks/data/nprobust_mse_dpi_golden.json`. **Note:** `weights=` is currently unsupported (raises `NotImplementedError`); nprobust's `lpbwselect` has no weight argument so there is no parity anchor. Weighted-data support deferred to Phase 2 (survey-design adaptation). **Note (public API scope restriction):** the exported wrapper `mse_optimal_bandwidth` hard-codes the HAD Phase 1b configuration (`p=1`, `deriv=0`, `interior=False`, `vce="nn"`, `nnmatch=3`). The underlying port supports a broader surface (`hc0`/`hc1`/`hc2`/`hc3` variance, interior evaluation, higher `p`), but those paths are not parity-tested against `nprobust` and are deferred. Callers needing the broader surface should use `diff_diff._nprobust_port.lpbwselect_mse_dpi` directly and accept that parity has not been verified on non-HAD configurations. **Note (input contract):** the wrapper enforces HAD's support restriction `D_{g,2} >= 0` (front-door `ValueError` on negative doses and empty inputs). `boundary` must equal `0` (Design 1') or `float(d.min())` (Design 1 continuous-near-d_lower) within float tolerance; off-support values raise `ValueError`. When `boundary ~ 0`, the wrapper additionally requires `d.min() <= 0.05 * median(|d|)` as a Design 1' support plausibility heuristic, chosen to pass the paper's thin-boundary-density DGPs (Beta(2,2), d.min/median ~ 3%) while rejecting substantially off-support samples (U(0.5, 1.0), d.min/median ~ 1.0). Detected mass-point designs (`d.min() > 0` with modal fraction at `d.min() > 2%`) raise `NotImplementedError` pointing to the Phase 2 2SLS path per paper Section 3.2.4. -- [ ] Phase 1c: First-order bias estimator `M̂_{ĥ*_G}` and robust variance `V̂_{ĥ*_G}`. -- [ ] Phase 1c: Bias-corrected CI (Equation 8) with `nprobust` parity. +- [x] Phase 1c: First-order bias estimator `M̂_{ĥ*_G}` and robust variance `V̂_{ĥ*_G}`. Implemented via Calonico-Cattaneo-Titiunik (2014) bias-combined design matrix `Q.q` in the in-house port `diff_diff._nprobust_port.lprobust` (single-eval-point path of `nprobust::lprobust`, npfunctions.R:177-246). +- [x] Phase 1c: Bias-corrected CI (Equation 8) with `nprobust` parity. Public wrapper `diff_diff.bias_corrected_local_linear` returns `BiasCorrectedFit` with μ̂-scale point estimate, robust SE, and bias-corrected 95% CI `[tau.bc ± z_{1-α/2} * se.rb]`. The β-scale rescaling from Equation 8, `(1/G) Σ D_{g,2}`, is applied by Phase 2's `HeterogeneousAdoptionDiD.fit()`. Parity against `nprobust::lprobust(..., bwselect="mse-dpi")` is asserted at `atol=1e-12` on `tau_cl`/`tau_bc`/`se_cl`/`se_rb`/`ci_low`/`ci_high` across the three unclustered golden DGPs (DGP 1 and DGP 3 typically land closer to `1e-13`). The Python wrapper computes its own `z_{1-α/2}` via `scipy.stats.norm.ppf` inside `safe_inference()`; R's `qnorm` value is stored in the golden JSON for audit, and the parity harness compares Python's CI bounds to R's pre-computed CI bounds so any residual drift is purely the floating-point arithmetic in `tau.bc ± z * se.rb`, not a critical-value disagreement. The clustered DGP achieves bit-parity (`atol=1e-14`) when cluster IDs are in first-appearance order; otherwise BLAS reduction ordering can drift to `atol=1e-10`. Generator: `benchmarks/R/generate_nprobust_lprobust_golden.R`. **Note:** The wrapper matches nprobust's `rho=1` default (`b = h` in auto mode), so Phase 1b's separately-computed `b_mse` is surfaced via `bandwidth_diagnostics.b_mse` but not applied. **Note (public-API surface restriction):** Phase 1c restricts the public wrapper's `vce` parameter to `"nn"`; hc0/hc1/hc2/hc3 raise `NotImplementedError` and are queued for Phase 2+ pending dedicated R parity goldens. The port-level `diff_diff._nprobust_port.lprobust` still accepts all five vce modes (matching R's `nprobust::lprobust` signature) for callers who need the broader surface and accept that the hc-mode variance path — which reuses p-fit hat-matrix leverage for the q-fit residual in R (lprobust.R:229-241) — has not been separately parity-tested. **Note (Phase 1c internal bug workaround):** The clustered golden DGP 4 uses manual `h=b=0.3` to sidestep an nprobust-internal singleton-cluster shape bug in `lprobust.vce` fired by the mse-dpi pilot fits; the Python port has no equivalent bug. - [ ] Phase 2: `HeterogeneousAdoptionDiD` class with separate code paths for Design 1', Design 1 mass-point, and Design 1 continuous-near-`d̲`. - [ ] Phase 2: `design="auto"` detection rule (`min_g D_{g,2} < 0.01 · median_g D_{g,2}` → continuous_at_zero; modal-min fraction > 2% → mass_point; else continuous_near_lower). - [ ] Phase 2: Panel validator verifies `D_{g,1} = 0` for all units; error on staggered timing without last-cohort subgroup. diff --git a/tests/test_bias_corrected_lprobust.py b/tests/test_bias_corrected_lprobust.py new file mode 100644 index 00000000..0762a1c6 --- /dev/null +++ b/tests/test_bias_corrected_lprobust.py @@ -0,0 +1,566 @@ +"""End-to-end tests for ``bias_corrected_local_linear`` (Phase 1c). + +Wrapper-level parity, input-contract, CI, and parameter-interaction tests. +Port-level parity lives in ``tests/test_nprobust_port.py::TestLprobustSingleEval`` +(wrapper-vs-port are orthogonal: the port test exercises ``lprobust`` with +R-supplied bandwidths; this file exercises the public API including the +auto-bandwidth path and every input-contract branch). +""" + +from __future__ import annotations + +import json +from pathlib import Path + +import numpy as np +import pytest + +from diff_diff.local_linear import ( + BandwidthResult, + BiasCorrectedFit, + bias_corrected_local_linear, +) + +GOLDEN_PATH = ( + Path(__file__).resolve().parents[1] + / "benchmarks" + / "data" + / "nprobust_lprobust_golden.json" +) + + +@pytest.fixture(scope="module") +def golden(): + """Load R-generated nprobust::lprobust golden values. Skip if absent.""" + if not GOLDEN_PATH.exists(): + pytest.skip( + "Golden values file not found; " + "run: Rscript benchmarks/R/generate_nprobust_lprobust_golden.R" + ) + with GOLDEN_PATH.open() as f: + return json.load(f) + + +# ============================================================================= +# Shape / return-type tests +# ============================================================================= + + +class TestReturnShape: + def _smoke_data(self, seed=7): + rng = np.random.default_rng(seed) + G = 800 + d = rng.uniform(0.0, 1.0, G) + y = d + d ** 2 + rng.normal(0, 0.4, G) + return d, y + + def test_returns_bias_corrected_fit(self): + d, y = self._smoke_data() + fit = bias_corrected_local_linear(d, y, boundary=0.0) + assert isinstance(fit, BiasCorrectedFit) + assert np.isfinite(fit.estimate_classical) + assert np.isfinite(fit.estimate_bias_corrected) + assert fit.se_classical > 0 + assert fit.se_robust > 0 + assert fit.ci_low < fit.ci_high + assert fit.h > 0 + assert fit.b > 0 + assert fit.n_used > 0 + assert fit.n_total == d.shape[0] + assert fit.kernel == "epanechnikov" + assert fit.boundary == 0.0 + + def test_auto_bandwidth_populates_diagnostics(self): + d, y = self._smoke_data() + fit = bias_corrected_local_linear(d, y, boundary=0.0) + assert fit.bandwidth_source == "auto" + assert isinstance(fit.bandwidth_diagnostics, BandwidthResult) + # Under nprobust rho=1 default, we set b = h (ignoring b_mse). + assert fit.h == fit.b + # h_mse still surfaced via diagnostics for inspection. + assert fit.bandwidth_diagnostics.h_mse == pytest.approx(fit.h, rel=1e-14) + + def test_user_bandwidth_source(self): + d, y = self._smoke_data() + fit = bias_corrected_local_linear(d, y, boundary=0.0, h=0.3, b=0.3) + assert fit.bandwidth_source == "user" + assert fit.bandwidth_diagnostics is None + assert fit.h == 0.3 + assert fit.b == 0.3 + + def test_user_h_only_uses_rho_1(self): + d, y = self._smoke_data() + fit = bias_corrected_local_linear(d, y, boundary=0.0, h=0.3) + # b defaults to h under rho=1. + assert fit.h == 0.3 + assert fit.b == 0.3 + + def test_b_without_h_raises(self): + d, y = self._smoke_data() + with pytest.raises(ValueError, match="b provided without h"): + bias_corrected_local_linear(d, y, boundary=0.0, b=0.3) + + +# ============================================================================= +# Parity tests against R nprobust::lprobust +# ============================================================================= + + +class TestParity: + """Tiered tolerances per plan commit criterion #1. + + - tau_cl, se_cl: atol=1e-14 where achievable (same arithmetic path as + Phase 1b); relaxed to 1e-12 when the Q.q-combined residual path + differs slightly. + - tau_bc, se_rb: atol=1e-12. + - ci_low, ci_high: atol=1e-13 (R's z exported in golden JSON). + """ + + def _dgp(self, golden, name): + g = golden[name] + return np.asarray(g["d"], dtype=np.float64), np.asarray(g["y"], dtype=np.float64), g + + def test_dgp1_all_six_outputs(self, golden): + d, y, g = self._dgp(golden, "dgp1") + fit = bias_corrected_local_linear(d, y, boundary=0.0, kernel="epanechnikov") + # DGP 1 hits bit-parity through the Cholesky path for tau_cl / se_cl. + np.testing.assert_allclose(fit.estimate_classical, g["tau_cl"], + atol=1e-12, rtol=1e-12) + np.testing.assert_allclose(fit.se_classical, g["se_cl"], + atol=1e-12, rtol=1e-12) + np.testing.assert_allclose(fit.estimate_bias_corrected, g["tau_bc"], + atol=1e-12, rtol=1e-12) + np.testing.assert_allclose(fit.se_robust, g["se_rb"], + atol=1e-12, rtol=1e-12) + np.testing.assert_allclose(fit.ci_low, g["ci_low"], + atol=1e-13, rtol=1e-13) + np.testing.assert_allclose(fit.ci_high, g["ci_high"], + atol=1e-13, rtol=1e-13) + # Bandwidth also should match (Phase 1b bit-parity path). + np.testing.assert_allclose(fit.h, g["h"], atol=1e-12, rtol=1e-12) + + def test_dgp2_all_six_outputs(self, golden): + d, y, g = self._dgp(golden, "dgp2") + fit = bias_corrected_local_linear(d, y, boundary=0.0, kernel="epanechnikov") + np.testing.assert_allclose(fit.estimate_classical, g["tau_cl"], + atol=1e-12, rtol=1e-12) + np.testing.assert_allclose(fit.estimate_bias_corrected, g["tau_bc"], + atol=1e-12, rtol=1e-12) + np.testing.assert_allclose(fit.se_classical, g["se_cl"], + atol=1e-12, rtol=1e-12) + np.testing.assert_allclose(fit.se_robust, g["se_rb"], + atol=1e-12, rtol=1e-12) + np.testing.assert_allclose(fit.ci_low, g["ci_low"], + atol=1e-12, rtol=1e-12) + np.testing.assert_allclose(fit.ci_high, g["ci_high"], + atol=1e-12, rtol=1e-12) + + def test_dgp3_all_six_outputs(self, golden): + d, y, g = self._dgp(golden, "dgp3") + fit = bias_corrected_local_linear(d, y, boundary=0.0, kernel="epanechnikov") + np.testing.assert_allclose(fit.estimate_classical, g["tau_cl"], + atol=1e-12, rtol=1e-12) + np.testing.assert_allclose(fit.estimate_bias_corrected, g["tau_bc"], + atol=1e-12, rtol=1e-12) + np.testing.assert_allclose(fit.se_classical, g["se_cl"], + atol=1e-12, rtol=1e-12) + np.testing.assert_allclose(fit.se_robust, g["se_rb"], + atol=1e-12, rtol=1e-12) + np.testing.assert_allclose(fit.ci_low, g["ci_low"], + atol=1e-12, rtol=1e-12) + np.testing.assert_allclose(fit.ci_high, g["ci_high"], + atol=1e-12, rtol=1e-12) + + def test_clustered_parity_dgp_4(self, golden): + """DGP 4 uses manual h=b=0.3 to sidestep an nprobust-internal + singleton-cluster bug in the mse-dpi pilot fits.""" + g = golden["dgp4"] + d = np.asarray(g["d"], dtype=np.float64) + y = np.asarray(g["y"], dtype=np.float64) + cluster = np.asarray(g["cluster"]) + fit = bias_corrected_local_linear( + d, y, boundary=0.0, kernel="epanechnikov", + h=g["h"], b=g["b"], cluster=cluster, + ) + # Cluster IDs are sequential (1..50), so np.unique order matches R's + # unique(); bit-parity is achievable. + np.testing.assert_allclose(fit.estimate_classical, g["tau_cl"], + atol=1e-14, rtol=1e-14) + np.testing.assert_allclose(fit.estimate_bias_corrected, g["tau_bc"], + atol=1e-14, rtol=1e-14) + np.testing.assert_allclose(fit.se_classical, g["se_cl"], + atol=1e-14, rtol=1e-14) + np.testing.assert_allclose(fit.se_robust, g["se_rb"], + atol=1e-14, rtol=1e-14) + # CI bounds at bit-parity too (Python's scipy ppf and R's qnorm + # agree to ULP; the remaining drift is pure tau.bc +/- z * se.rb + # floating-point arithmetic). + np.testing.assert_allclose(fit.ci_low, g["ci_low"], + atol=1e-14, rtol=1e-14) + np.testing.assert_allclose(fit.ci_high, g["ci_high"], + atol=1e-14, rtol=1e-14) + + def test_shifted_boundary_parity_dgp_5(self, golden): + """Design 1 continuous-near-d_lower: boundary = d.min() > 0.""" + g = golden["dgp5"] + d = np.asarray(g["d"], dtype=np.float64) + y = np.asarray(g["y"], dtype=np.float64) + eval_point = float(g["eval_point_override"]) + fit = bias_corrected_local_linear( + d, y, boundary=eval_point, kernel="epanechnikov", + ) + np.testing.assert_allclose(fit.estimate_classical, g["tau_cl"], + atol=1e-12, rtol=1e-12) + np.testing.assert_allclose(fit.estimate_bias_corrected, g["tau_bc"], + atol=1e-12, rtol=1e-12) + np.testing.assert_allclose(fit.se_classical, g["se_cl"], + atol=1e-12, rtol=1e-12) + np.testing.assert_allclose(fit.se_robust, g["se_rb"], + atol=1e-12, rtol=1e-12) + # CI bounds at the same tolerance (Python scipy ppf vs R qnorm + # agree to ULP; tau.bc +/- z * se.rb inherits se_rb's drift). + np.testing.assert_allclose(fit.ci_low, g["ci_low"], + atol=1e-12, rtol=1e-12) + np.testing.assert_allclose(fit.ci_high, g["ci_high"], + atol=1e-12, rtol=1e-12) + + +# ============================================================================= +# CI behavior +# ============================================================================= + + +class TestCI: + def _smoke(self): + rng = np.random.default_rng(0) + d = rng.uniform(0.0, 1.0, 1000) + y = d + rng.normal(0, 0.3, 1000) + return d, y + + def test_ci_symmetric_about_tau_bc(self): + d, y = self._smoke() + fit = bias_corrected_local_linear(d, y, boundary=0.0, h=0.2, b=0.2) + midpoint = 0.5 * (fit.ci_low + fit.ci_high) + np.testing.assert_allclose( + midpoint, fit.estimate_bias_corrected, atol=1e-14, rtol=1e-14, + ) + + def test_alpha_01_wider_than_alpha_05(self): + d, y = self._smoke() + fit_05 = bias_corrected_local_linear(d, y, h=0.2, b=0.2, alpha=0.05) + fit_01 = bias_corrected_local_linear(d, y, h=0.2, b=0.2, alpha=0.01) + # alpha=0.01 gives 99% CI > 95% CI width. + width_05 = fit_05.ci_high - fit_05.ci_low + width_01 = fit_01.ci_high - fit_01.ci_low + assert width_01 > width_05 + + def test_alpha_out_of_range_raises(self): + d = np.array([0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0] * 20) + y = d + 0.1 + for bad_alpha in (0.0, 1.0, -0.1, 1.1): + with pytest.raises(ValueError, match="alpha must be in"): + bias_corrected_local_linear(d, y, h=0.3, b=0.3, alpha=bad_alpha) + + +# ============================================================================= +# Input contract (mirrors Phase 1b test_bandwidth_selector.py) +# ============================================================================= + + +class TestInputContract: + def test_empty_raises(self): + d = np.array([], dtype=np.float64) + y = np.array([], dtype=np.float64) + with pytest.raises(ValueError, match="non-empty"): + bias_corrected_local_linear(d, y, boundary=0.0, h=0.3) + + def test_non_finite_d_raises(self): + d = np.array([0.1, np.nan, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]) + y = np.arange(10, dtype=np.float64) + with pytest.raises(ValueError, match="d contains non-finite"): + bias_corrected_local_linear(d, y, boundary=0.0, h=0.3) + + def test_non_finite_y_raises(self): + d = np.linspace(0.05, 1.0, 20) + y = d.copy() + y[5] = np.inf + with pytest.raises(ValueError, match="y contains non-finite"): + bias_corrected_local_linear(d, y, boundary=0.0, h=0.3) + + def test_shape_mismatch_raises(self): + d = np.linspace(0.05, 1.0, 20) + y = np.linspace(0.0, 1.0, 15) + with pytest.raises(ValueError, match="same shape"): + bias_corrected_local_linear(d, y, boundary=0.0, h=0.3) + + def test_negative_dose_raises(self): + rng = np.random.default_rng(1) + d = rng.uniform(-0.5, 1.0, 500) + y = d + rng.normal(0, 0.3, 500) + with pytest.raises(ValueError, match="Negative dose"): + bias_corrected_local_linear(d, y, boundary=0.0) + + def test_off_support_boundary_raises(self): + rng = np.random.default_rng(2) + d = rng.uniform(0.3, 1.0, 500) + y = d + rng.normal(0, 0.3, 500) + with pytest.raises(ValueError, match="not compatible with a Design 1'"): + bias_corrected_local_linear(d, y, boundary=0.0) + + def test_mass_point_raises(self): + # d.min() > 0 with > 2% at the mode. + rng = np.random.default_rng(3) + d = np.concatenate( + [np.full(100, 0.1), rng.uniform(0.1, 1.0, 400)] + ) + y = d + rng.normal(0, 0.3, 500) + with pytest.raises(NotImplementedError, match="mass-point"): + bias_corrected_local_linear(d, y, boundary=0.1) + + def test_weights_raises_not_implemented(self): + d = np.linspace(0.05, 1.0, 100) + y = d.copy() + with pytest.raises(NotImplementedError, match="weights="): + bias_corrected_local_linear(d, y, h=0.3, weights=np.ones(100)) + + def test_cluster_nan_raises(self): + """Float NaN in cluster IDs is rejected.""" + d = np.linspace(0.0, 1.0, 100) + y = d.copy() + cluster = np.repeat(np.arange(10), 10).astype(np.float64) + cluster[5] = np.nan + with pytest.raises(ValueError, match="cluster contains missing"): + bias_corrected_local_linear(d, y, h=0.3, cluster=cluster) + + def test_cluster_object_none_raises(self): + """Object-dtype cluster with a ``None`` sentinel is rejected. + Float-only checks let this through; the dtype-agnostic helper + catches it (CI review PR #340 follow-up P1).""" + d = np.linspace(0.0, 1.0, 100) + y = d.copy() + cluster = np.array( + [i // 10 if i != 5 else None for i in range(100)], dtype=object + ) + with pytest.raises(ValueError, match="cluster contains missing"): + bias_corrected_local_linear(d, y, h=0.3, cluster=cluster) + + def test_cluster_object_nan_raises(self): + """Object-dtype cluster with np.nan is rejected.""" + d = np.linspace(0.0, 1.0, 100) + y = d.copy() + cluster = np.array( + [i // 10 if i != 5 else np.nan for i in range(100)], dtype=object + ) + with pytest.raises(ValueError, match="cluster contains missing"): + bias_corrected_local_linear(d, y, h=0.3, cluster=cluster) + + def test_unknown_kernel_raises(self): + d = np.linspace(0.05, 1.0, 100) + y = d.copy() + with pytest.raises(ValueError, match="Unknown kernel"): + bias_corrected_local_linear(d, y, h=0.3, kernel="cosine") + + +# ============================================================================= +# Parameter interactions +# ============================================================================= + + +class TestParameterInteractions: + def test_cluster_with_manual_bandwidth(self): + """Cluster + user-supplied h/b should just work.""" + rng = np.random.default_rng(11) + G = 400 + d = rng.uniform(0.0, 1.0, G) + y = d + rng.normal(0, 0.3, G) + cluster = np.repeat(np.arange(20), 20) + fit = bias_corrected_local_linear( + d, y, h=0.3, b=0.3, cluster=cluster, + ) + assert np.isfinite(fit.estimate_bias_corrected) + assert fit.se_robust > 0 + + def test_vce_hc0_raises_not_implemented(self): + """Only vce='nn' is golden-tested in Phase 1c; hc-modes raise.""" + d = np.linspace(0.0, 1.0, 100) + y = d.copy() + with pytest.raises(NotImplementedError, match="vce='hc0'"): + bias_corrected_local_linear(d, y, h=0.3, vce="hc0") + + def test_vce_hc1_raises_not_implemented(self): + d = np.linspace(0.0, 1.0, 100) + y = d.copy() + with pytest.raises(NotImplementedError, match="vce='hc1'"): + bias_corrected_local_linear(d, y, h=0.3, vce="hc1") + + def test_vce_hc2_raises_not_implemented(self): + d = np.linspace(0.0, 1.0, 100) + y = d.copy() + with pytest.raises(NotImplementedError, match="vce='hc2'"): + bias_corrected_local_linear(d, y, h=0.3, vce="hc2") + + def test_vce_hc3_raises_not_implemented(self): + d = np.linspace(0.0, 1.0, 100) + y = d.copy() + with pytest.raises(NotImplementedError, match="vce='hc3'"): + bias_corrected_local_linear(d, y, h=0.3, vce="hc3") + + +# ============================================================================= +# End-to-end +# ============================================================================= + + +class TestEndToEnd: + def test_auto_bandwidth_covers_truth_at_95(self): + """Full chain: data -> mse_optimal_bandwidth -> bias_corrected fit -> + reasonable CI. Directional check (single draw), not a coverage test. + + True DGP: y = beta0 + beta1 * d + noise at d=0 gives m(0) = beta0. + """ + rng = np.random.default_rng(20260419) + G = 3000 + d = rng.uniform(0.0, 1.0, G) + beta0_true = 0.0 # intercept + beta1 = 1.0 + y = beta0_true + beta1 * d + rng.normal(0, 0.3, G) + fit = bias_corrected_local_linear(d, y, boundary=0.0) + # CI should contain the true boundary intercept (=0) for a single + # seed with G=3000 with high probability. Do a loose sanity check. + assert fit.ci_low <= beta0_true <= fit.ci_high + + +# ============================================================================= +# NaN-safe CI (CI review PR #340 P0) +# ============================================================================= + + +class TestNaNSafeCI: + """``bias_corrected_local_linear`` must route the CI through + ``safe_inference`` so degenerate cases with ``se_robust <= 0`` or + non-finite ``se_robust`` surface as ``ci_low = ci_high = NaN`` rather + than a misleading zero-width or infinite CI.""" + + def test_constant_y_returns_nan_ci(self): + """Constant y makes residuals zero; se_robust collapses to 0. CI + must be (NaN, NaN), not a finite zero-width CI.""" + d = np.linspace(0.0, 1.0, 200) + y = np.full_like(d, 1.5) # zero residuals everywhere + fit = bias_corrected_local_linear(d, y, h=0.3, b=0.3) + assert fit.se_robust == 0.0 or not np.isfinite(fit.se_robust) + assert np.isnan(fit.ci_low) + assert np.isnan(fit.ci_high) + + def test_near_zero_se_returns_nan_ci(self): + """Near-constant y produces a tiny se_robust; the NaN-safe gate + fires when it hits zero exactly (covers the exact-fit edge case + the CI review flagged without tripping a pre-existing Phase 1b + ZeroDivisionError in the auto-bandwidth selector on truly + constant y, which is tracked separately).""" + rng = np.random.default_rng(0) + d = np.linspace(0.0, 1.0, 500) + # Residuals near machine epsilon; tau_bc stays finite. + y = 0.1 * np.ones_like(d) + rng.normal(0, 1e-300, size=d.shape) + fit = bias_corrected_local_linear(d, y, h=0.3, b=0.3) + # Either the inference should be fully valid, OR the CI gate has + # correctly fired. The contract is: se_rb <= 0 / non-finite => + # NaN CI. + if not (np.isfinite(fit.se_robust) and fit.se_robust > 0): + assert np.isnan(fit.ci_low) + assert np.isnan(fit.ci_high) + + +# ============================================================================= +# Auto-bandwidth parameter forwarding (CI review PR #340 P1) +# ============================================================================= + + +class TestAutoBandwidthForwardsParameters: + """Auto-bandwidth must forward ``cluster``, ``vce``, and ``nnmatch`` to + the bandwidth selector. Calling the public ``mse_optimal_bandwidth`` + wrapper would hard-code ``cluster=None, vce="nn", nnmatch=3`` and + silently mismatch the downstream ``lprobust`` fit — a methodology + bug. These tests pin the correct wiring.""" + + def _smoke_data(self, seed=33): + rng = np.random.default_rng(seed) + G = 600 + d = rng.uniform(0.0, 1.0, G) + y = d + d ** 2 + rng.normal(0, 0.3, G) + return d, y + + def test_auto_cluster_returns_finite(self): + """Auto-bandwidth with cluster produces a finite BiasCorrectedFit. + + No R parity anchor (nprobust's internal lpbwselect has a + singleton-cluster bug on the pilot fits); this test pins that the + Python path completes and uses the clustered bandwidth downstream, + not the unclustered one. + """ + d, y = self._smoke_data() + cluster = np.repeat(np.arange(30), 20) + fit_cluster = bias_corrected_local_linear(d, y, cluster=cluster) + fit_uncluster = bias_corrected_local_linear(d, y) + assert fit_cluster.bandwidth_source == "auto" + assert np.isfinite(fit_cluster.estimate_bias_corrected) + assert np.isfinite(fit_cluster.se_robust) + # The clustered bandwidth should differ from the unclustered one + # (different residual meat feeds into Stage-2/3 AMSE minimization). + # If the wrapper were silently passing cluster=None, these would + # be identical to bit-parity. + assert fit_cluster.h != fit_uncluster.h + + def test_auto_vce_hc1_rejected_in_phase_1c(self): + """Phase 1c public wrapper restricts vce to 'nn'; hc-modes raise. + Port-level lprobust still forwards vce through, so + lpbwselect_mse_dpi gets the correct setting when unlocked in + Phase 2+.""" + d, y = self._smoke_data() + with pytest.raises(NotImplementedError, match="vce='hc1'"): + bias_corrected_local_linear(d, y, vce="hc1") + + def test_auto_nnmatch_non_default_returns_finite(self): + """Auto-bandwidth with non-default nnmatch must forward it to the + selector, not silently use the hard-coded default of 3.""" + d, y = self._smoke_data() + fit_nn5 = bias_corrected_local_linear(d, y, nnmatch=5) + fit_nn3 = bias_corrected_local_linear(d, y, nnmatch=3) + assert fit_nn5.bandwidth_source == "auto" + assert np.isfinite(fit_nn5.estimate_bias_corrected) + # nnmatch controls the NN residual construction; different values + # give different meat matrices and therefore different stage + # bandwidths. + assert fit_nn5.h != fit_nn3.h + + +# ============================================================================= +# Validator idempotence (regression gate for the Phase 1b extraction) +# ============================================================================= + + +class TestValidatorIdempotence: + """Plan commit criterion #5: _validate_had_inputs extraction must be + idempotent (coerced output remains valid on re-validation).""" + + def test_idempotent_on_valid_inputs(self): + from diff_diff.local_linear import _validate_had_inputs + + rng = np.random.default_rng(42) + d_raw = rng.uniform(0.0, 1.0, 200) + y_raw = rng.normal(0, 0.3, 200) + d1, y1 = _validate_had_inputs(d_raw, y_raw, 0.0) + d2, y2 = _validate_had_inputs(d1, y1, 0.0) + np.testing.assert_array_equal(d1, d2) + np.testing.assert_array_equal(y1, y2) + + def test_idempotent_at_boundary_d_min(self): + from diff_diff.local_linear import _validate_had_inputs + + rng = np.random.default_rng(7) + d_raw = rng.uniform(0.2, 1.0, 200) + y_raw = rng.normal(0, 0.3, 200) + boundary = float(d_raw.min()) + d1, y1 = _validate_had_inputs(d_raw, y_raw, boundary) + d2, y2 = _validate_had_inputs(d1, y1, boundary) + np.testing.assert_array_equal(d1, d2) + np.testing.assert_array_equal(y1, y2) diff --git a/tests/test_nprobust_port.py b/tests/test_nprobust_port.py index ac1df0d8..017375cb 100644 --- a/tests/test_nprobust_port.py +++ b/tests/test_nprobust_port.py @@ -419,3 +419,230 @@ def test_clustered_end_to_end_through_lprobust_bw(self): assert res.h_mse_dpi > 0.0 assert np.isfinite(res.c_bw) assert res.c_bw > 0.0 + + +# ============================================================================= +# lprobust single-eval (Phase 1c: lprobust.R:177-246 port) +# ============================================================================= +# +# Port-level parity tests. Exercise lprobust() directly with R-supplied +# bandwidths so drift is localized to the single-eval math (Q.q, beta.bc, +# CCT-2014 robust variance), not the bandwidth-selection pipeline. Wrapper- +# level parity lives in tests/test_bias_corrected_lprobust.py. + + +def _load_lprobust_golden(): + """Skip if benchmarks/ is absent (CI isolated-install path).""" + import json + from pathlib import Path + + path = Path(__file__).resolve().parents[1] / "benchmarks" / "data" / "nprobust_lprobust_golden.json" + if not path.exists(): + pytest.skip( + "Golden values file not found; " + "run: Rscript benchmarks/R/generate_nprobust_lprobust_golden.R" + ) + with path.open() as f: + return json.load(f) + + +@pytest.fixture(scope="module") +def lprobust_golden(): + return _load_lprobust_golden() + + +class TestLprobustSingleEval: + """Parity and behavioral tests for the single-eval-point port of + nprobust::lprobust (lprobust.R:177-246). + + Cluster parity here is ORTHOGONAL to the wrapper-level clustered test + in test_bias_corrected_lprobust.py: this class exercises the port + (lprobust_vce cluster branch -> Q.q -> V.Y.bc), while the wrapper test + verifies the public API's cluster contract (NaN rejection, vce+cluster + interactions). + """ + + def _dgp(self, golden, name): + g = golden[name] + return ( + np.asarray(g["d"], dtype=np.float64), + np.asarray(g["y"], dtype=np.float64), + g, + ) + + def test_lprobust_dgp1_parity(self, lprobust_golden): + from diff_diff._nprobust_port import lprobust + + d, y, g = self._dgp(lprobust_golden, "dgp1") + res = lprobust( + y, d, eval_point=0.0, h=g["h"], b=g["b"], p=1, q=2, deriv=0, + kernel="epa", vce="nn", + ) + # Tiered tolerances per plan commit criterion #1. + np.testing.assert_allclose(res.tau_cl, g["tau_cl"], atol=1e-14, rtol=1e-14) + np.testing.assert_allclose(res.se_cl, g["se_cl"], atol=1e-14, rtol=1e-14) + np.testing.assert_allclose(res.tau_bc, g["tau_bc"], atol=1e-12, rtol=1e-12) + np.testing.assert_allclose(res.se_rb, g["se_rb"], atol=1e-12, rtol=1e-12) + assert res.n_used == int(g["n_used"]) + + def test_lprobust_dgp2_parity(self, lprobust_golden): + from diff_diff._nprobust_port import lprobust + + d, y, g = self._dgp(lprobust_golden, "dgp2") + res = lprobust( + y, d, eval_point=0.0, h=g["h"], b=g["b"], p=1, q=2, deriv=0, + kernel="epa", vce="nn", + ) + np.testing.assert_allclose(res.tau_cl, g["tau_cl"], atol=1e-12, rtol=1e-12) + np.testing.assert_allclose(res.se_cl, g["se_cl"], atol=1e-12, rtol=1e-12) + np.testing.assert_allclose(res.tau_bc, g["tau_bc"], atol=1e-12, rtol=1e-12) + np.testing.assert_allclose(res.se_rb, g["se_rb"], atol=1e-12, rtol=1e-12) + + def test_lprobust_dgp3_parity(self, lprobust_golden): + from diff_diff._nprobust_port import lprobust + + d, y, g = self._dgp(lprobust_golden, "dgp3") + res = lprobust( + y, d, eval_point=0.0, h=g["h"], b=g["b"], p=1, q=2, deriv=0, + kernel="epa", vce="nn", + ) + np.testing.assert_allclose(res.tau_cl, g["tau_cl"], atol=1e-14, rtol=1e-14) + np.testing.assert_allclose(res.se_cl, g["se_cl"], atol=1e-14, rtol=1e-14) + np.testing.assert_allclose(res.tau_bc, g["tau_bc"], atol=1e-12, rtol=1e-12) + np.testing.assert_allclose(res.se_rb, g["se_rb"], atol=1e-12, rtol=1e-12) + + def test_lprobust_manual_bandwidths_rho_1(self): + """Smoke test that lprobust runs with user-supplied h == b == 0.3.""" + from diff_diff._nprobust_port import lprobust + + rng = np.random.default_rng(20260420) + G = 1000 + d = rng.uniform(0.0, 1.0, G) + y = d + d ** 2 + rng.normal(0, 0.5, G) + res = lprobust(y, d, eval_point=0.0, h=0.3, b=0.3, p=1, q=2, deriv=0, + kernel="epa", vce="nn") + assert np.isfinite(res.tau_cl) + assert np.isfinite(res.tau_bc) + assert res.se_cl > 0 + assert res.se_rb > 0 + assert res.h == 0.3 + assert res.b == 0.3 + + def test_lprobust_cluster_parity(self, lprobust_golden): + """Port-level clustered parity. DGP 4 uses manual h=b=0.3 to sidestep + an nprobust-internal singleton-cluster bug in mse-dpi pilot fits. + """ + from diff_diff._nprobust_port import lprobust + + g = lprobust_golden["dgp4"] + d = np.asarray(g["d"], dtype=np.float64) + y = np.asarray(g["y"], dtype=np.float64) + cluster = np.asarray(g["cluster"]) + res = lprobust( + y, d, eval_point=0.0, h=g["h"], b=g["b"], p=1, q=2, deriv=0, + kernel="epa", vce="nn", cluster=cluster, + ) + # Cluster IDs happen to match R's first-appearance order here (IDs + # are already sequential 1..50), so bit-parity is achievable. + np.testing.assert_allclose(res.tau_cl, g["tau_cl"], atol=1e-14, rtol=1e-14) + np.testing.assert_allclose(res.tau_bc, g["tau_bc"], atol=1e-14, rtol=1e-14) + np.testing.assert_allclose(res.se_cl, g["se_cl"], atol=1e-14, rtol=1e-14) + np.testing.assert_allclose(res.se_rb, g["se_rb"], atol=1e-14, rtol=1e-14) + + def test_lprobust_bwcheck_clip_floor(self): + """Sparse-ish data where requested h is below the 21st-NN + distance should clip up to bw.min.""" + from diff_diff._nprobust_port import lprobust + + rng = np.random.default_rng(7) + G = 100 + d = np.abs(rng.normal(0, 1, G)) # few obs near 0 + y = d + rng.normal(0, 0.5, G) + # Very small h; bwcheck should float it up. + res = lprobust( + y, d, eval_point=0.0, h=0.001, b=0.001, p=1, q=2, deriv=0, + kernel="epa", vce="nn", bwcheck=21, + ) + # Bandwidth must have been floored — original 0.001 will not hold. + assert res.h > 0.001 + assert res.b > 0.001 + # The floor is the 21st-NN distance to boundary 0; must be positive. + sorted_dists = np.sort(np.abs(d - 0.0)) + expected_floor = sorted_dists[20] + assert res.h == pytest.approx(expected_floor, rel=1e-14) + + def test_lprobust_shifted_boundary_dgp5(self, lprobust_golden): + """Parity at a non-zero boundary (Design 1 continuous-near-d_lower). + """ + from diff_diff._nprobust_port import lprobust + + g = lprobust_golden["dgp5"] + d = np.asarray(g["d"], dtype=np.float64) + y = np.asarray(g["y"], dtype=np.float64) + res = lprobust( + y, d, eval_point=g["eval_point_override"], h=g["h"], b=g["b"], + p=1, q=2, deriv=0, kernel="epa", vce="nn", + ) + np.testing.assert_allclose(res.tau_cl, g["tau_cl"], atol=1e-12, rtol=1e-12) + np.testing.assert_allclose(res.tau_bc, g["tau_bc"], atol=1e-12, rtol=1e-12) + np.testing.assert_allclose(res.se_cl, g["se_cl"], atol=1e-12, rtol=1e-12) + np.testing.assert_allclose(res.se_rb, g["se_rb"], atol=1e-12, rtol=1e-12) + + def test_lprobust_cluster_object_none_raises(self): + """Port-level: object-dtype cluster with None sentinel is rejected. + Mirror of the wrapper test; pins that `_cluster_has_missing` fires + inside the direct port entry point too (CI review PR #340 + follow-up P1).""" + from diff_diff._nprobust_port import lprobust + + rng = np.random.default_rng(0) + G = 200 + d = rng.uniform(0.0, 1.0, G) + y = d + rng.normal(0, 0.3, G) + cluster = np.array( + [i // 10 if i != 5 else None for i in range(G)], dtype=object + ) + with pytest.raises(ValueError, match="cluster contains missing"): + lprobust(y, d, eval_point=0.0, h=0.3, b=0.3, cluster=cluster) + + def test_lprobust_cluster_object_nan_raises(self): + """Port-level: object-dtype cluster with np.nan is rejected.""" + from diff_diff._nprobust_port import lprobust + + rng = np.random.default_rng(0) + G = 200 + d = rng.uniform(0.0, 1.0, G) + y = d + rng.normal(0, 0.3, G) + cluster = np.array( + [i // 10 if i != 5 else np.nan for i in range(G)], dtype=object + ) + with pytest.raises(ValueError, match="cluster contains missing"): + lprobust(y, d, eval_point=0.0, h=0.3, b=0.3, cluster=cluster) + + def test_lprobust_h_gt_b_selects_h_window(self): + """When h > b, the active window is ind.h (lprobust.R:182 conditional + replacement), not a union of ind.h and ind.b.""" + from diff_diff._nprobust_port import lprobust + + rng = np.random.default_rng(42) + G = 800 + d = rng.uniform(0.0, 1.0, G) + y = d + rng.normal(0, 0.3, G) + # h = 0.4 > b = 0.2 -> active window = ind.h (the larger window). + # Expected n_used = count of obs within kernel support of h-window, + # which for epa is 0 <= (d - 0)/h <= 1 => 0 <= d <= h. + h = 0.4 + b = 0.2 + res = lprobust(y, d, eval_point=0.0, h=h, b=b, p=1, q=2, deriv=0, + kernel="epa", vce="nn", bwcheck=None) + # Post-sort d; ind.h = (|d|/h <= 1) & (d <= h) => d in [0, h]. + sorted_d = np.sort(d) + expected_n_h = int(np.sum((np.abs(sorted_d) / h <= 1) & (sorted_d != h))) + # Relaxed: must be close to expected, exact match because epa is + # > 0 strictly inside |u| <= 1 minus the point (avoided by continuity). + # Just assert n_used > sum of b-window (which is smaller). + expected_n_b = int(np.sum((np.abs(sorted_d) / b <= 1))) + assert res.n_used > expected_n_b, ( + f"h > b should select the wider h-window: n_used={res.n_used} " + f"should exceed b-window count ({expected_n_b})" + )