This directory contains Jupyter notebook tutorials demonstrating the features of the diff-diff library.

Introduction to Difference-in-Differences with diff-diff:

• Basic 2x2 DiD estimation
• Column-name and formula interfaces
• Adding covariates
• Fixed effects (dummy and absorbed)
• Two-Way Fixed Effects (TWFE)
• Cluster-robust standard errors
• Wild cluster bootstrap

Handling staggered treatment adoption with the Callaway-Sant'Anna estimator:

• Understanding staggered adoption
• Problems with TWFE in staggered settings
• Goodman-Bacon decomposition: Diagnosing why TWFE fails
• Group-time effects ATT(g,t)
• Aggregation methods (simple, group, event-study)
• Control group specifications
• Visualization

Synthetic Difference-in-Differences for few treated units:

• When to use Synthetic DiD
• Understanding unit and time weights
• Pre-treatment fit diagnostics
• Inference methods (bootstrap, placebo)
• Regularization tuning
• Comparison with standard DiD

Testing assumptions and diagnostics:

• Visual inspection of trends
• Simple parallel trends tests
• Robust Wasserstein-based tests
• Equivalence testing (TOST)
• Placebo tests (timing, group, permutation)
• Event study as a diagnostic
• What to do if parallel trends fails

Efficient Difference-in-Differences (Chen, Sant'Anna & Xie 2025):

• Optimal weighting across comparison groups and baselines
• PT-All vs PT-Post assumptions
• Efficiency gains vs Callaway-Sant'Anna
• Event study and group-level aggregation
• Bootstrap inference and diagnostics

Wooldridge Extended Two-Way Fixed Effects (ETWFE) for staggered DiD:

• Basic OLS estimation with cohort x time ATT cells
• Aggregation methods: event-study, group, calendar, simple
• Poisson QMLE for count / non-negative outcomes
• Logit for binary outcomes
• Comparison with Callaway-Sant'Anna
• Delta-method standard errors

Survey-aware DiD with complex sampling designs (strata, PSU, FPC, weights):

• Why survey design matters for DiD inference
• Setting up SurveyDesign (weights, strata, PSU, FPC)
• Basic DiD and staggered DiD with survey design
• Replicate weights (JK1, BRR, Fay, JKn)
• Subpopulation analysis
• DEFF diagnostics
• Repeated cross-sections with survey design

1. Install diff-diff with dependencies:

pip install diff-diff
pip install matplotlib  # for visualizations
pip install jupyter     # to run notebooks

2. Start Jupyter:

jupyter notebook

3. Open any notebook and run the cells.

• Python 3.8+
• diff-diff
• numpy
• pandas
• matplotlib (optional, for visualizations)