Presented by: Andrew Goodman-Bacon, Ph.D., M.A. Senior Research Economist Opportunity & Inclusive Growth Institute

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. When researchers go beyond that simple case, however, empirical practices can be ad hoc and unreliable. This article provides an organizing framework for defining different types of DiD designs and then deriving theoretically grounded estimators. It discusses covariates, multiple periods, staggered treatments, and continuous treatments. The broad approach, however, applies to any other extensions of DiD methods.