Treatment Effects: The Basics

About

This course will introduce the basics of causal inference using observational data, including real-world applications in the R programming language. Topics will include selection on observables, bad controls, directed acyclic graphs (DAGs), instrumental variables, local average treatment effects, regression discontinuity, and difference-in-difference methods. At the end of this course, the students will have a good foundation for further study of causal inference, including our summer course offering ‘Treatment Effects: Beyond the Basics’ in September.

TOPICS COVERED:
- Probability review, R review
- Selection-on-observables, DAGs and Bad Controls
- Regression Discontinuity
- Instrumental Variables and Local Average Treatment Effects
- Difference-in-Differences

The course can be attended either in person or online.

Course leader

Francis DiTraglia, Associate Professor of Economics

Target group

This course is a foundation course aimed at undergraduate and graduate students who have taken a first course in statistics and probability and those in industry or policy with an economics degree.

Fee info