Causal Inference with Differences-in-Differences

A comprehensive, rigorous introduction to modern differences-in-differences (DID) estimators, covering both standard practices and alternatives

Differences-in-differences (DID) is one of the most widely used methods for impact-evaluation in economics and the social sciences. The key idea behind DiD is to compare outcomes trends for treated and control groups, allowing researchers to estimate the effects of policies or interventions when randomized experiments are not feasible. This book provides a clear and rigorous guide to modern DID methods, covering both classical approaches and newer estimators developed for complex real-world settings. Designed for advanced undergraduate students, graduate students, and applied researchers, it explains when standard methods are reliable, when they can mislead, and how alternative approaches can provide more credible results. Throughout, theoretical discussion is paired with empirical applications, exercises using real datasets, and practical recommendations for implementation.

The book offers:
• Discussion of all designs in which DID apply: classical designs, staggered adoption designs, designs with variation in treatment dose, and staggered first switch designs
• Study of standard estimators, estimators without parallel trends (e.g., synthetic controls), and heterogeneity-robust estimators
• 5 lists of dos and don’ts for practitioners
• 150 exercises to understand the theory, and 50 practical exercises to apply it to real empirical examples in Stata and R