Difference-in-Differences Designs

Difference-in-Differences (DiD) methods are widely used to answer what-if type of questions in economics, political science, and many other social and medical sciences. These methods are also very popular in industry, especially in tech companies, where causal inference play a prominent role.

The main goal of this course is to provide a fast-track towards these best practices, enabling each and every attendee to be comfortable with a wide range of DiD tools.

As we believe that the best way to really learn any data science tool is to blend its theory with real-life applications, each lecture session will include a hands-on exercise that illustrates the content covered. We will provide practical guidance on implementing these tools in R and Stata, whenever possible.