Abstract:

This course examines how to learn cause-and-effect relationships from data in statistics, social sciences, and artificial intelligence. It emphasizes why intuition alone can be misleading without a formal framework for causation. Topics include potential outcomes and counterfactuals, measures of treatment effects, causal graphical models, confounding adjustment, instrumental variables, principal stratification, mediation, and interference. Concepts will be illustrated with real examples from computer science, social science, and biomedical research.