Causal Inference

4-0-8 units · Undergraduate

Provides an accessible overview of modern quantitative methods for causal inference: testing whether an action causes an outcome to occur. Makes heavy use of applied, real-data examples using Python or R and drawn from the participating domains (economics, political science, business, public policy, etc.). Covers topics including potential outcomes, causal graphs, randomized controlled trials, observational studies, instrumental variable estimation, and a contrast with machine learning techniques. Seeks to provide an intuitive understanding of the core concepts and techniques to help students produce and consume evidence of causal claims.