Difference in Differences Methods

Difference-in-Differences (DiD) is a popular method for estimating causal effects of treatments or policy interventions in non-experimental settings using longitudinal data. The “canonical” DiD model involves two periods (pre- and post-treatment) and two groups (treatment and control), but recent methodological advances have extended DiD to handle situations with multiple periods and variation in treatment timing. This workshop will give a practical introduction to traditional and modern DiD methods, including an overview of their key assumptions, guidance on when and how to use them, and illustrations of their use through worked examples in R and Stata.