Welcome to the 4th course in our series on causal inference concepts and methods created by Duke University with support from eBay, Inc. Designed to teach you causal inference concepts, methods, and how to code in R with realistic data, this course focuses on how to use the difference-in-differences method to find causal effects in panel data, how you need to argue their validity, and what they look like in practice. We’ll stay away from dense statistical math and focus instead on higher level concepts that data scientists need to always consider when examining and making inferences about data. The course instructors and creators are Dr. Matt Masten (Duke University), James Speckart (Duke), Brian Aronson (Duke), and Alexandra Cooper (Duke).
Introduction to Panel Data
This chapter will introduce you to analysis techniques with panel data
Looking Deeper at Panel Data
This chapter takes a deeper dive into the terms and choices you need to be aware of when working with panel data.