Bayesian Causal Forest: Foundations & Application

3.0 credits

Bayesian causal forest (BCF) is a powerful approach for inferring causal effects and identifying sources of heterogeneity. This course first builds the statistical foundation required for these complex models, covering single- and multi-level linear models, Bayesian regression models, and regression tree ensembles. With this groundwork established, the remainder of the course is dedicated to learning and applying BCF. In addition to teaching these statistical techniques, a core objective in the class is to build students’ fluency coding in R. No prior programming experience is required.