Fall 2023: Causality and Machine Learning

This course introduces the fundamental concepts of causality, and causal inference using machine learning models. Topics will include:counterfactuals (potential outcomes and graphs), identification and estimation of conditional average treatment effects from randomized control trials and observational data, as well as causal inference under hidden confounding and limited overlap.