CLRES 2036 - INTRODUCTION TO CAUSAL INFERENCE

Minimum Credits: 1
Maximum Credits: 1
The course will present an introduction to the concepts and framework in causal inference. In the lectures, causal models will be depicted using directed acyclic graphs (DAG) and defined with nonparametric structural equation models (NPSEM) while target causal parameters will be defined using counterfactuals, principle stratification, and marginal structural models. We will also introduce propensity score modeling, g-computation estimators, and inverse probability weighted estimators. Students will gain practical experience implementing these estimators and learn how to interpret results through in-class discussions and Stata assignments.
Academic Career: Graduate
Course Component: Lecture
Grade Component: Grad LG/SNC Basis

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