University of Iowa 3 credits DATA
Introduce methods for reasoning about causes, effects, and bias when analyzing experimental and observational data. Topics include the potential outcomes framework, counterfactuals, confounding, and missing data; the identification and estimation of causal effects via propensity score methods, marginal structural models, instrumental variables, and directed acyclic graphs; as well as applications of machine learning and Bayesian methods to causal inference. Prerequisites: (DATA:3120 or STAT:3120) and (DATA:3200 or STAT:3200). Requirements: familiarity with the R programming. Same as STAT:4600.
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