feat: port twfeweights/ptetools/badcontrols R packages (R-compat) - #753
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wenddymacro wants to merge 53 commits into
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feat: port twfeweights/ptetools/badcontrols R packages (R-compat)#753wenddymacro wants to merge 53 commits into
wenddymacro wants to merge 53 commits into
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added 30 commits
August 6, 2026 09:23
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August 6, 2026 14:47
- process_dose_gt consumes an R-style gt_results dict + ptep options and returns a complete DoseResult: ATT(d)/ACRT(d) curves, per-dose multiplier- bootstrap SEs, pointwise/simultaneous critical values, and overall ATT/ACRT with SEs and influence functions. - bspline_basis reproduces splines2::bSpline / dbs exactly (clamped boundary knots, intercept=False drops first basis column, derivative via the knot/ coefficient transform); golden parity pinned against live R output. - mboot_se_and_crit turns mboot2 draws into R-style IQR bootstrap SEs and a sup-t critical value using R quantile(type=1). - DoseResult extended to the full dose_obj surface while keeping pte_dose_results backward-compatible; new exports + docs + CHANGELOG entry. - tests/test_ptetools_process_dose_gt.py: splines2 golden parity, knot validation, end-to-end point estimates, seed reproducibility, order and missing-field rejection.
Port the quantile-treatment-effects machinery and extend the twfeweights/badcontrols/ptetools R-compat layer: - pte_qtt / PTEQTTResult, compute_pte (g,t) loop, qtt/qott aggregation, qtt_empirical_bootstrap super-t bands, block_boot_sample, _qtt_crit_val - ggpte / ggpte_cont event-study and dose plotting wrappers plus autoplot/plot methods on PTE, QTT, emp-boot, and dose result objects - attgt_noif container, covid_attgt DRDID levels/changes score - PTEResults.aggregate() influence-function SEs, CIs, to_dataframe levels, dynamic multiplier-bootstrap bands - dr_ml_attgt bad-controls cell wrapper, mp_weights_obj twfeweights support - _NotSupplied copy/deepcopy for result containers
R's badcontrols::dr_ml_attgt always cross-fits the parametric nuisances (OLS m/omega, logit p), so a full-sample Python fit was not fold-mirror parity: different set.seed gave different R ATs but the Python call gave one. This makes dr_parametric_bad_control cross-fit like R: - split folds per treatment arm (treated/control each see every fold) - m0/nu0/omega0 OLS + p2 logit fit on the training folds, evaluated on the held-out fold; in-sample fitted outcomes feed the nu/omega targets. - ingress fold_ids (validated 0..n_folds-1) for exact shared-fold parity. - keep the max(propensity)>0.99 -> imputation fallback guard from R. New tests pin fold-dependence, fold_ids reproducibility/validation, the imputation fallback, and gt_data from two_by_two_subset. REGISTRY gains a Bad Controls section; grf-vs-sklearn ML nuisance documented as not parity-able. mypy note: env fails on numpy 2.5.1 .pyi under 3.12 target 3.10 (pre- existing, not from this change).
…licit weights The R parity harness pins twfe_weights/attO_weights/att_simple_weights to 1e-8. Two other twfeweights functions cannot be byte-parity-tested and are now documented as REGISTRY deviations rather than claimed silently: - did_post_lasso: the R reference source is incomplete (carries a browser() debug path); Python's LassoCV AIPW is a faithful reading of the paper, verified for internal consistency only. - implicit_twfe_weights: fixest segfaults on the parity fixture, so no stable R reference number exists; the closed-form FWL decomposition is verified for self-consistency (alpha_weight*attgt reconstructs TWFE). Adds a twfe_weights methodology section + TOC entry and a CHANGELOG note.
…1) like R The high-level pte() wrapper built its influence surface with NaN for off-support units and no (n/n1) sample-size correction, diverging from R's compute.pte (ptetools/R/pte.R:137-141), which zero-pads with rep(0, n); this.inf_func[disidx] <- (n/n1)*attgt. The lower-level compute_pte already matched R; the wrapper did not. pte() now: - zero-fills off-support unit entries (not NaN) - scales each cell influence function by (n / n1) for overall-vs-cell sizes - keeps base-period-skip cells as a full-NA column (as both R and compute_pte do) New test pins the (units, cells) surface: no NaN anywhere, off-support entries zero, and placed entries equal (n/n1)*did_attgt(...).inf_func per estimable cell. REGISTRY gains a ptetools influence-surface note.
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Summary
Ports the full public API of the R packages
twfeweights,ptetools, andbadcontrolsintodiff_diff/, with R/Python numeric parity on the implemented subset.Scope
twfe_weights(incl. AIPW + balance diagnostics) + post-lasso blockpte()main loop,did_attgt, full QTT/QoTT block,process_dose_gt(splines2-compatiblebspline_basis),mboot2/mboot_se_and_crit, RCS/dose/mboot,covid_attgt,attgt_noif, aggregations, and matplotlib/Plotly plotting wrappers (ggpte,ggpte_cont,plot_qtt,autoplot/plotcounterparts)R parity verified
twfe_weights,ptetools did_attgt,badcontrolscontinuous + binary imputationqtt_crit_valto ~1e-13; overall QTT on single-cohort R panel to 0.0bspline_basismatchessplines2::bSpline/dbsexactly (golden values pinned in tests)implicit_twfe_weights— R fixest segfaults on fixture;did_post_lasso— R source contains abrowser()debug path)Tests
New-feature suites (
tests/test_{ptetools,twfeweights,badcontrols}_*.py+test_r_parity_new_features.py): 82 passed underDIFF_DIFF_BACKEND=python. Ruff/black/mypy clean.