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-diff

For 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_getting_started
   practitioner_decision_tree

.. toctree::
   :maxdepth: 2
   :caption: Getting Started
   :hidden:

   quickstart
   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

.. 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

.. 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, and fixest — see :doc:`benchmarks`
• No heavy dependencies: Only numpy, pandas, and scipy

• :ref:`genindex`
• :ref:`modindex`
• :ref:`search`