diff-diff is a Python library for Difference-in-Differences (DiD) causal inference analysis. It provides 19 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; the underlying design-based variance methodology is derived in the companion preprint [@Gerber2026]. Point estimates are validated against established R packages to machine precision, with standard errors matching exactly or to sub-percent relative differences.

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 19 estimators with a consistent API, survey-weighted inference, and numerical validation against R. It is also the companion software for the design-based variance framework of @Gerber2026, which establishes design-consistent standard errors for modern DiD estimators under complex survey designs. It targets both applied researchers who need rigorous econometric methods and data science practitioners who need accessible causal inference tools integrated into Python workflows.

Breadth of methods. diff-diff implements 19 estimators organized across the modern DiD taxonomy. Classic designs include two-group/two-period DiD, two-way fixed effects, and event-study estimation with period-specific effects. Heterogeneity-robust staggered-adoption estimators include Callaway-Sant'Anna [@Callaway2021], Sun-Abraham [@Sun2021], imputation [@Borusyak2024], two-stage [@Gardner2022], stacked [@Wing2024], and efficient [@Chen2025] approaches, together with reversible-treatment DiD for non-absorbing interventions [@deChaisemartin2020] and a ring-indicator estimator for spatial spillovers [@Butts2021]. Synthetic-control hybrids include synthetic DiD [@Arkhangelsky2021] and the classic synthetic control method [@Abadie2010]. Extended designs include triple-difference and staggered triple-difference estimators [@OrtizVillavicencio2025], continuous-treatment DiD with dose-response curves [@Callaway2024], heterogeneous-adoption designs where no unit remains untreated [@deChaisemartin2026], nonlinear ETWFE [@Wooldridge2025; @Wooldridge2023], and triply robust panel estimation [@Athey2025]. Separate diagnostic and sensitivity tools - outside the 19 estimators - include Goodman-Bacon decomposition [@GoodmanBacon2021], Honest DiD sensitivity analysis [@Rambachan2023], placebo tests, 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 real complex-survey datasets - NHANES, RECS 2020, and the California API school dataset - to a tight tolerance (test gaps < 1e-8, typically below 1e-10). The design-based variance result - that the influence functions of modern DiD estimators satisfy Binder's (1983) smoothness conditions, so stratified-cluster linearization yields design-consistent standard errors - is derived in @Gerber2026. 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.

Generative AI tools were used in developing this software and manuscript. Anthropic's Claude models (the Opus and Sonnet families, via the Claude Code CLI) assisted with code generation and refactoring, test scaffolding, documentation, and drafting and editing of this manuscript. The author reviewed, modified, and validated all AI-generated code and text and made all primary architectural and methodological decisions. Numerical results were independently verified against established R reference packages (did, synthdid, fixest, survey) for every estimator with an R equivalent, and against the author's reference derivations or simulation otherwise. The author takes full responsibility for the accuracy and integrity of the software and this paper.