1 · The DiD idea
Why comparing one group's before/after change against a control group's change isolates a causal effect.
Difference-in-Differences
Treatment effects from before/after × treated/control, parallel trends, event studies.
Course overview
Learn Difference-in-Differences interactively
This free course uses real Python and statsmodels in your browser. Work through the modules below with explanations, executable examples, interactive controls, and questions.
2 · The 2×2 estimator
The DiD estimate is exactly the interaction coefficient in a regression of y on treated, post, and their product.
3 · Parallel trends
DiD is valid only if the groups would have moved in parallel absent treatment; a pre-trend difference biases the ATT.
4 · Event-study / dynamic DiD
Estimate a separate effect for each period relative to treatment to test pre-trends and trace the effect's dynamics.