Difference-in-differences (DID) designs are commonly used to evaluate the changes in outcomes associated with health care policy implementation. The basic definition of a DID design is “the difference in the pre-post mean for the treatment group minus the difference in the pre-post mean for the control group.” This talk will describe the key assumptions underlying DID and how these methods are implemented. It will also discuss common extensions to the basic model as well as limitations.