diff-diff: Difference-in-Differences in Python

diff-diff is a Python library for Difference-in-Differences (DiD) causal inference analysis. It provides sklearn-like estimators with statsmodels-style output for econometric analysis.

from diff_diff import DifferenceInDifferences

# Fit a basic DiD model
did = DifferenceInDifferences()
results = did.fit(data, outcome='y', treated='treated', post='post')
print(results.summary())

Key Features

  • Multiple Estimators: Basic DiD, Two-Way Fixed Effects, Multi-Period Event Studies, Synthetic DiD, and Callaway-Sant’Anna for staggered adoption

  • Modern Inference: Robust standard errors, cluster-robust SEs, and wild cluster bootstrap

  • Assumption Testing: Parallel trends tests, placebo tests, and comprehensive diagnostics

  • Sensitivity Analysis: Honest DiD (Rambachan & Roth 2023) for robust inference under parallel trends violations

  • Publication-Ready Output: Summary tables and event study plots

Installation

pip install diff-diff

For development:

pip install diff-diff[dev]

Indices and tables