Applied Machine Learning and Causal Inference

Undergraduate

We cover approaches to causal inference using machine learning. Machine learning methods include bagging, boosting, random forests, and neural networks. Causal topics include randomized experiments with and without noncompliance, observational studies with and without ignorable treatment assignment, instrumental variables, and regression discontinuity. Assignments provide students with hands-on experience with the methods. Applications are drawn from a variety of fields including political science, economics, public health, and medicine. Programming is central to the course and is based on the R programming language. Prerequisites: The equivalent of at least two of the following courses: S&DS 230, 238, 241 and 242; previous programming experience (e.g., R, Matlab, Python, C++), R preferred. Strong knowledge of OLS is assumed. SO Th 4pm-5:55pm