Indiana University Bloomington

Statistics S681
Statistical Methods For Causal Inference

Offered: Spring, 2016
Class Days: Tu, Th
Pre-Requisites: A solid understanding of logistic regression, preferable at the level of Stat 503 or Soc 650.
Instructor: Weihua An
Syllabus: No Syllabus Avaliable
Description: Correlation is not causation. The quest for causation has formed a main stream in contemporary statistics. Based on the potential outcomes framework, this course presents the state-of-art of statistical methods for causal inference. The topics to be covered include inference in randomized experiments, matching and propensity score methods, directed acyclic graphs, instrumental variable methods, regression discontinuity designs, causal inference in panel data, causal mediation analysis, causal analysis under interference, etc. A variety of examples are drawn from social and medical sciences to illustrate the methods.