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This documentation is for the development version of diff-diff. It may describe features not yet available in the latest PyPI release. Use the version selector to switch to stable.

diff-diff

  • Practitioner Guide
  • Decision Tree
  • Getting Started
  • Estimator Guide
  • Troubleshooting
    • References
    • Measuring Campaign Impact on Brand Awareness with Survey Data
    • Tutorial 18: Geo-Experiment Analysis with SyntheticDiD
    • Tutorial 19: dCDH for Marketing Pulse Campaigns
    • Tutorial 20: HAD for a National Brand Campaign with Regional Spend Intensity
    • Tutorial 21: HAD Pre-test Workflow - Running the Pre-test Diagnostics on the Brand Campaign Panel
    • Tutorial 22: Survey-Weighted HAD - The BRFSS-Shape Rollout
    • Spillover-aware DiD with SpilloverDiD — a TVA-style worked example
    • Basic Difference-in-Differences with diff-diff
    • Staggered Difference-in-Differences
    • Synthetic Difference-in-Differences (SDID)
    • Tutorial 8: Triple Difference (DDD) Estimation
    • Real-World Data Examples
    • Triply Robust Panel (TROP) Estimator
    • Imputation DiD (Borusyak, Jaravel & Spiess 2024)
    • Two-Stage DiD (Gardner 2022)
    • Stacked DiD (Wing, Freedman & Hollingsworth 2024)
    • Continuous Difference-in-Differences
    • Efficient DiD (Chen, Sant’Anna & Xie 2025)
    • Survey-Aware Difference-in-Differences
    • Wooldridge Extended Two-Way Fixed Effects (ETWFE)
    • Testing Parallel Trends and DiD Diagnostics
    • Honest DiD: Sensitivity Analysis for Parallel Trends
    • Power Analysis for Difference-in-Differences
    • Pre-Trends Power Analysis (Roth 2022)
    • Tutorial 24: Staggered Rollout or a Simple 2×2? A Power-Analysis Decision Guide
    • R Comparison
    • Python Comparison
    • Benchmarks
    • API Reference
  • GitHub
  • PyPI
  • Practitioner Guide
  • Decision Tree
  • Getting Started
  • Estimator Guide
  • Troubleshooting
  • References
  • Measuring Campaign Impact on Brand Awareness with Survey Data
  • Tutorial 18: Geo-Experiment Analysis with SyntheticDiD
  • Tutorial 19: dCDH for Marketing Pulse Campaigns
  • Tutorial 20: HAD for a National Brand Campaign with Regional Spend Intensity
  • Tutorial 21: HAD Pre-test Workflow - Running the Pre-test Diagnostics on the Brand Campaign Panel
  • Tutorial 22: Survey-Weighted HAD - The BRFSS-Shape Rollout
  • Spillover-aware DiD with SpilloverDiD — a TVA-style worked example
  • Basic Difference-in-Differences with diff-diff
  • Staggered Difference-in-Differences
  • Synthetic Difference-in-Differences (SDID)
  • Tutorial 8: Triple Difference (DDD) Estimation
  • Real-World Data Examples
  • Triply Robust Panel (TROP) Estimator
  • Imputation DiD (Borusyak, Jaravel & Spiess 2024)
  • Two-Stage DiD (Gardner 2022)
  • Stacked DiD (Wing, Freedman & Hollingsworth 2024)
  • Continuous Difference-in-Differences
  • Efficient DiD (Chen, Sant’Anna & Xie 2025)
  • Survey-Aware Difference-in-Differences
  • Wooldridge Extended Two-Way Fixed Effects (ETWFE)
  • Testing Parallel Trends and DiD Diagnostics
  • Honest DiD: Sensitivity Analysis for Parallel Trends
  • Power Analysis for Difference-in-Differences
  • Pre-Trends Power Analysis (Roth 2022)
  • Tutorial 24: Staggered Rollout or a Simple 2×2? A Power-Analysis Decision Guide
  • R Comparison
  • Python Comparison
  • Benchmarks
  • API Reference
  • GitHub
  • PyPI
  • Overview: module code

All modules for which code is available

  • diff_diff.bacon
  • diff_diff.business_report
  • diff_diff.chaisemartin_dhaultfoeuille
  • diff_diff.chaisemartin_dhaultfoeuille_results
  • diff_diff.continuous_did
  • diff_diff.continuous_did_results
  • diff_diff.datasets
  • diff_diff.diagnostic_report
  • diff_diff.diagnostics
  • diff_diff.efficient_did
  • diff_diff.efficient_did_bootstrap
  • diff_diff.efficient_did_results
  • diff_diff.estimators
  • diff_diff.had
  • diff_diff.had_pretests
  • diff_diff.honest_did
  • diff_diff.imputation
  • diff_diff.imputation_results
  • diff_diff.local_linear
  • diff_diff.power
  • diff_diff.prep
  • diff_diff.prep_dgp
  • diff_diff.pretrends
  • diff_diff.profile
  • diff_diff.results
  • diff_diff.spillover
  • diff_diff.stacked_did
  • diff_diff.stacked_did_results
  • diff_diff.staggered
  • diff_diff.staggered_bootstrap
  • diff_diff.staggered_results
  • diff_diff.staggered_triple_diff
  • diff_diff.staggered_triple_diff_results
  • diff_diff.sun_abraham
  • diff_diff.synthetic_control
  • diff_diff.synthetic_control_results
  • diff_diff.synthetic_did
  • diff_diff.triple_diff
  • diff_diff.trop
  • diff_diff.trop_results
  • diff_diff.twfe
  • diff_diff.two_stage
  • diff_diff.two_stage_results
  • diff_diff.utils
  • diff_diff.visualization._continuous
  • diff_diff.visualization._diagnostic
  • diff_diff.visualization._event_study
  • diff_diff.visualization._power
  • diff_diff.visualization._staggered
  • diff_diff.visualization._synthetic
  • diff_diff.wooldridge
  • diff_diff.wooldridge_results

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