This course introduces the research area of causal inference in the intersection of statistics, social science, and artificial intelligence. A central theme of this course will be that without a formal theory of causation, intuition alone can be misleading for drawing causal conclusions. 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 applications in a wide range of subjects, such as computer science, social science, and biomedical data science.