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', treatment='treated', time='post')
print(results.summary())- 13+ Estimators: Basic DiD, TWFE, Event Study, Synthetic DiD, plus modern staggered estimators (Callaway-Sant'Anna, Sun-Abraham, Imputation, Two-Stage, Stacked DiD), advanced methods (TROP, Continuous DiD, Efficient DiD, Triple Difference), and Bacon Decomposition diagnostics
- Modern Inference: Robust standard errors, cluster-robust SEs, wild cluster bootstrap, and multiplier bootstrap
- Assumption Testing: Parallel trends tests, placebo tests, Bacon decomposition, and comprehensive diagnostics
- Sensitivity Analysis: Honest DiD (Rambachan & Roth 2023) for robust inference under parallel trends violations
- Built-in Datasets: Real-world datasets from published studies (Card & Krueger, Castle Doctrine, and more)
- High Performance: Optional Rust backend for compute-intensive estimators like Synthetic DiD and TROP
- Publication-Ready Output: Summary tables, event study plots, and sensitivity analysis figures
pip install diff-diffFor development:
pip install diff-diff[dev]- :doc:`practitioner_getting_started` - Measuring campaign impact? Start here
- :doc:`practitioner_decision_tree` - Which method fits your business problem?
- :doc:`quickstart` - Installation and your first DiD analysis
- :doc:`choosing_estimator` - Which estimator should I use?
- :func:`~diff_diff.aggregate_survey` - Have BRFSS/ACS/CPS microdata? Bridge it to a geographic panel for DiD
- :doc:`tutorials/01_basic_did` - Hands-on basic tutorial
- :doc:`troubleshooting` - Common issues and solutions
- :doc:`r_comparison` - Coming from R?
- :doc:`api/index` - Full API reference
.. toctree:: :maxdepth: 2 :caption: For Data Scientists :hidden: Practitioner Guide <practitioner_getting_started> Decision Tree <practitioner_decision_tree>
.. toctree:: :maxdepth: 2 :caption: Getting Started :hidden: quickstart Estimator Guide <choosing_estimator> troubleshooting references
.. toctree:: :maxdepth: 1 :caption: Tutorials: Business Applications :hidden: tutorials/17_brand_awareness_survey tutorials/18_geo_experiments tutorials/19_dcdh_marketing_pulse tutorials/20_had_brand_campaign tutorials/21_had_pretest_workflow tutorials/22_had_survey_design tutorials/23_spillover_tva
.. toctree:: :maxdepth: 1 :caption: Tutorials: Fundamentals :hidden: tutorials/01_basic_did tutorials/02_staggered_did tutorials/03_synthetic_did tutorials/08_triple_diff tutorials/09_real_world_examples
.. toctree:: :maxdepth: 1 :caption: Tutorials: Advanced Methods :hidden: tutorials/10_trop tutorials/11_imputation_did tutorials/12_two_stage_did tutorials/13_stacked_did tutorials/14_continuous_did tutorials/15_efficient_did tutorials/16_survey_did tutorials/16_wooldridge_etwfe
.. toctree:: :maxdepth: 1 :caption: Tutorials: Study Design :hidden: tutorials/04_parallel_trends tutorials/05_honest_did tutorials/06_power_analysis tutorials/07_pretrends_power tutorials/24_staggered_vs_collapsed_power
.. toctree:: :maxdepth: 1 :caption: Comparisons & Benchmarks :hidden: r_comparison python_comparison benchmarks
.. toctree:: :maxdepth: 2 :caption: API Reference :hidden: api/index
Difference-in-Differences (DiD) is a quasi-experimental research design that estimates causal treatment effects by comparing outcome changes over time between treated and control groups. It is one of the most widely used methods in applied economics, public policy evaluation, and social science research.
- Complete method coverage: 13+ estimators from basic 2x2 DiD to cutting-edge methods like Efficient DiD (Chen et al. 2025) and TROP (Athey et al. 2025)
- Familiar API: sklearn-like
fit()interface — if you know scikit-learn, you know diff-diff - Modern staggered methods: Callaway-Sant'Anna, Sun-Abraham, Imputation DiD, Two-Stage DiD, and Stacked DiD handle heterogeneous treatment timing correctly
- Robust inference: Heteroskedasticity-robust, cluster-robust, wild cluster bootstrap, and multiplier bootstrap
- Sensitivity analysis: Honest DiD (Rambachan & Roth 2023) for robust inference under parallel trends violations
- Validated against R: Benchmarked against
did,synthdid, andfixest— see :doc:`benchmarks` - No heavy dependencies: Only numpy, pandas, and scipy
| Estimator | Description |
|---|---|
| :class:`~diff_diff.DifferenceInDifferences` | Basic 2x2 DiD with robust/clustered standard errors |
| :class:`~diff_diff.TwoWayFixedEffects` | Panel data with unit and time fixed effects |
| :class:`~diff_diff.MultiPeriodDiD` | Event study with period-specific treatment effects |
| :class:`~diff_diff.CallawaySantAnna` | Callaway & Sant'Anna (2021) for staggered adoption |
| :class:`~diff_diff.SunAbraham` | Sun & Abraham (2021) interaction-weighted estimator |
| :class:`~diff_diff.ImputationDiD` | Borusyak, Jaravel & Spiess (2024) imputation estimator |
| :class:`~diff_diff.TwoStageDiD` | Gardner (2022) two-stage residualized estimator |
| :class:`~diff_diff.SpilloverDiD` | Butts (2021) ring-indicator spillover-aware DiD |
| :class:`~diff_diff.SyntheticDiD` | Synthetic DiD combining DiD and synthetic control |
| :class:`~diff_diff.StackedDiD` | Wing, Freedman & Hollingsworth (2024) stacked DiD |
| :class:`~diff_diff.EfficientDiD` | Chen, Sant'Anna & Xie (2025) efficient DiD |
| :class:`~diff_diff.TripleDifference` | Triple difference (DDD) estimator |
| :class:`~diff_diff.ContinuousDiD` | Continuous treatment DiD |
| :class:`~diff_diff.TROP` | Triply Robust Panel with factor model adjustment (Athey et al. 2025) |
| :class:`~diff_diff.BaconDecomposition` | Goodman-Bacon decomposition diagnostics |