did-multiplegt-stat 0.1.0

Heterogeneity-robust difference-in-differences (DID) estimators (AS, WAS, IV-WAS) with stayers, for binary, discrete, or continuous treatments. Python port of the Stata `did_multiplegt_stat` package.

did_multiplegt_stat

Python implementation of the did_multiplegt_stat Stata package by de Chaisemartin, D'Haultfœuille, Pasquier, Sow, and Vazquez-Bare (2024) — heterogeneity-robust difference-in-differences estimators with stayers for binary, discrete, or continuous treatments (and instruments).

The package estimates the Average Slope (AS), Weighted Average Slope (WAS), and IV-WAS parameters in static designs where parallel trends is assumed conditional on the baseline treatment.

Installation

pip install did-multiplegt-stat

Optional extras:

pip install "did-multiplegt-stat[linearmodels]"   # alternative IV backend
pip install "did-multiplegt-stat[docs]"           # build docs locally
pip install "did-multiplegt-stat[dev]"            # tests + tooling

Quick start

import pandas as pd
from did_multiplegt_stat import DIDMultiplegtStat

df = pd.read_stata("gazoline_did_multiplegt_stat.dta")

# AS + WAS with 3 placebo periods (mirrors Stata example 2)
model = DIDMultiplegtStat(
    estimator=["aoss", "waoss"],
    order=1,
    placebo=3,
    aoss_vs_waoss=True,
)
model.fit(df, Y="lngca", ID="id", Time="year", D="tau")
model.summary()
model.plot()

Functional API (one-shot, mirrors the Stata command):

from did_multiplegt_stat import did_multiplegt_stat, summary_did_multiplegt_stat

res = did_multiplegt_stat(df, Y="lngca", ID="id", Time="year", D="tau",
                          estimator=["aoss", "waoss"], placebo=3,
                          aoss_vs_waoss=True)
summary_did_multiplegt_stat(res)

Backends: asinstata

Two regression backends are bundled:

Backend	When to use	Activate
scikit-learn (default)	Default behaviour. Faster, modern numerical stack.	asinstata=False
Stata-faithful	Need byte-for-byte parity with the Stata ado-file.	asinstata=True
Custom sklearn-style	Want a RandomForest, LassoCV, etc. as the nuisance.	model_deltay=..., model_stayer=...

Stata parity uses statsmodels OLS + a from-scratch Newton-Raphson logit that matches Stata's logit, asis defaults; results agree to ~1e-7 relative error.

What's supported

All Stata options are exposed, including:

• estimator (aoss / waoss / ivwaoss)
• estimation_method (ra / ps / dr)
• order (scalar, 4-tuple, or 8-tuple for IV)
• placebo(N) (multi-period placebos)
• exact_match, noextrapolation
• switchers (up / down)
• aoss_vs_waoss
• by, by_fd, by_baseline
• controls, weight, cluster
• other_treatments
• cross_fitting, trimming, on_placebo_sample
• bootstrap + seed
• twfe (with same_sample, full_sample, percentile)
• cross_validation (k-fold CV for polynomial order)

See the full documentation for the help-file style reference.

Citation

If you use this software in academic work, please cite the underlying paper:

de Chaisemartin, C., D'Haultfœuille, X., Pasquier, F., Sow, D., Vazquez-Bare, G. (2024). Difference-in-Differences for Continuous Treatments and Instruments with Stayers. arXiv:2201.06898.

A CITATION.cff is bundled for tooling integration.

License

GPL-3.0-or-later — see LICENSE.

Authors

Originally authored by the team behind the Stata package:

• Clément de Chaisemartin (Sciences Po)
• Diego Ciccia (Sciences Po)
• Xavier D'Haultfœuille (CREST-ENSAE)
• Felix Knau (Sciences Po)
• Felix Pasquier (CREST-ENSAE)
• Doulo Sow (Sciences Po)
• Gonzalo Vazquez-Bare (UCSB)

Python port: Anzony Quispe.

Contact: [e-mail]