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BIOSTAT 904 - Causal Inference

Comprehensive introduction to methods for causal inference. Principles necessary to define causation. Strategies such as directed acyclic graphs to identify potential sources of bias, when using observational data to address causal questions. Alternative approaches for the estimation of causal parameters and applications to practical applications. Methods for causal inference regarding treatments initiated during observational, longitudinal follow-up. Mathematical results fundamental to classic statistical methods. Development of simulations to implement and test methods. Prerequisite: Statistical Science 711, Statistical Science 721, Statistical Science 732 or consent of instructor. Instructor: Thomas
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