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title diff-diff: Comprehensive Difference-in-Differences Causal Inference for Python
tags
difference-in-differences
causal-inference
econometrics
Python
treatment-effects
survey-data
authors
name orcid affiliation
Isaac Gerber
0009-0009-3275-5591
1
affiliations
name index
Independent Researcher
1
date 12 April 2026
bibliography paper.bib

Summary

diff-diff is a Python library for Difference-in-Differences (DiD) causal inference analysis. It provides 17 estimators covering the full modern DiD toolkit - from classic two-group/two-period designs through heterogeneity-robust staggered adoption methods, synthetic control hybrids, and sensitivity analysis - under a consistent scikit-learn-style API. Most estimators accept an optional SurveyDesign object for design-based variance estimation with complex survey data, a capability absent from existing DiD software in any language. Point estimates are validated against established R packages to machine precision, with standard errors matching exactly or to sub-percent relative differences.

Statement of Need

Difference-in-differences is the most widely used quasi-experimental research design in applied economics and the social sciences. Since 2018, a wave of methodological advances has addressed fundamental limitations of the conventional two-way fixed effects (TWFE) estimator under staggered treatment adoption and heterogeneous effects [@Roth2023]. These modern methods - including Callaway and Sant'Anna [-@Callaway2021], Sun and Abraham [-@Sun2021], Borusyak, Jaravel, and Spiess [-@Borusyak2024], and others - are now standard practice in applied work.

The R ecosystem provides mature implementations across several packages: did [@Callaway2021], fixest [@Berge2018], synthdid [@Arkhangelsky2021], and HonestDiD [@Rambachan2023]. Stata offers csdid and didregress. Python, however, lacks a unified DiD library. Practitioners working in Python-based data science workflows - increasingly common in industry settings for marketing measurement, product experimentation, and policy evaluation - must either context-switch to R, reimplement methods from scratch, or rely on partial implementations scattered across unrelated packages.

diff-diff fills this gap by providing a single-import library that covers 17 estimators with a consistent API, survey-weighted inference, and numerical validation against R. It targets both applied researchers who need rigorous econometric methods and data science practitioners who need accessible causal inference tools integrated into Python workflows.

Key Features

Breadth of methods. diff-diff implements 17 estimators organized across the modern DiD taxonomy: classic DiD and TWFE; heterogeneity-robust staggered estimators including Callaway-Sant'Anna [@Callaway2021], Sun-Abraham [@Sun2021], imputation [@Borusyak2024], two-stage [@Gardner2022], stacked [@Wing2024], and efficient [@Chen2025] approaches; extended designs including synthetic DiD [@Arkhangelsky2021], triple difference [@OrtizVillavicencio2025], continuous treatment [@Callaway2024], nonlinear ETWFE [@Wooldridge2025; @Wooldridge2023], and triply robust panel estimation [@Athey2025]; reversible-treatment DiD for non-absorbing interventions [@deChaisemartin2020]; and diagnostics including Goodman-Bacon decomposition [@GoodmanBacon2021], Honest DiD sensitivity analysis [@Rambachan2023], and pre-trends power analysis [@Roth2022]. All estimators share a consistent fit() interface with get_params()/set_params() for configuration, R-style formula support, and rich results objects with summary() output. An optional Rust backend via PyO3 accelerates compute-intensive operations.

Survey-weighted inference. A SurveyDesign class supports stratification, primary sampling units, finite population corrections, and probability weights. Variance estimation includes Taylor series linearization, five replicate weight methods (BRR, Fay's BRR, JK1, JKn, SDR), and survey-aware bootstrap. Survey variance is validated against R's survey package [@Lumley2004] on three federal datasets (NHANES, RECS, API) to machine precision (differences < 1e-10). No other DiD package in any language provides integrated survey support.

Validation against R. Point estimates match the R did, synthdid, and fixest packages to machine precision (differences < 1e-10). Standard errors match exactly for core estimators including Callaway-Sant'Anna and basic DiD. Validation includes the canonical MPDTA minimum-wage dataset from Callaway and Sant'Anna [-@Callaway2021].

Practitioner tooling. Beyond estimation, diff-diff includes a practitioner decision tree for estimator selection, an 8-step diagnostic workflow based on Baker et al. [-@Baker2025], AI agent integration with structured next-steps guidance, and microdata aggregation utilities for converting individual-level survey responses into geographic-period panels suitable for DiD analysis.

Acknowledgments

Wenli Xu (Faculty of Finance, City University of Macau) implemented the WooldridgeDiD (ETWFE) estimator, including saturated OLS, logit, and Poisson QMLE paths with ASF-based ATT and delta-method standard errors. Development was assisted by Claude Code (Anthropic).

References