Causal Modeling

4 credits

Construction of causal hypotheses. Theories of causation, counterfactuals, intervention vs. passive observation. Contexts for causal inference: randomized experiments; sequential randomization; partial compliance; natural experiments, passive observation. Path diagrams, conditional independence, and d-separation. Model equivalence and causal under-determination. Prerequisite: two introductory applied statistics courses at the level of SOC 504 and SOC 505 (or equivalent). Offered: jointly with STAT 566.