Difference-in-Differences is one of the most widely applied methods for estimating causal effects of programs when the program was not implemented as a randomized controlled trial. In this video I describe the situations where the method is applicable and give you the intuition behind it. I also explain how and why you might want to use regression to estimate diff-in-diff effects. Throughout, I talk about the key assumption required for the diff-in-diff estimate to be valid. Intended audience: Folks who have had some exposure to linear regression models, but want to learn more statistical methods.