Difference-in-Differences Designs: A Practitioner’s Guide
Andrew Baker, Brantly Callaway, Scott Cunningham, Andrew Goodman-Bacon, Pedro H. C. Sant’Anna
Abstract
Difference-in-differences (DiD) is arguably the most popular quasi-experimental research design. Its canonical form, with two groups and two periods, is well understood. However, empirical practices can be ad hoc when researchers go beyond that simple case. This article provides an organizing framework for discussing different types of DiD designs and their associated DiD estimators. It discusses covariates, weights, handling multiple periods, and staggered treatments. The organizational framework, however, applies to other extensions of DiD methods as well. (JEL C23, H75, I12, I38)
Topics & Concepts
Simple (philosophy)Computer scienceEmpirical researchManagement sciencePost hocKnowledge managementData scienceOperations researchEpistemologyArtificial intelligenceSociologyAdvanced Causal Inference TechniquesBehavioral and Psychological StudiesStatistical Methods in Clinical Trials