Causal Inference Methods for Observational Data

Learning about cause and effect is a goal of most research, but obtaining valid estimates of causal effects can be difficult, especially with observational data. This workshop will introduce a toolbox of frequently used methods for causal inference, with an emphasis on developing an understanding of how and why the methods work, when to use them, and how to implement them. Worked examples of each method will be shown in R. Methods covered will include:

  • Regression
  • Difference-in-differences
  • Instrumental variables
  • Regression discontinuity

The workshop is designed for people who have some experience with linear regression and are familiar with causal graphs, which are covered in the Introduction to Causal Inference workshop.