Econometrics and Causal Design

2.5 credits

The course provides an overview of key methods for causal inference, including randomized experiments, observational studies, and modern techniques using machine learning. It covers both foundational frameworks (e.g., Neyman-Rubin and Pearl’s approach), as well as advanced tools like instrumental variables. Building on GBA511, the course provides DBA students with an advanced treatment of causal inference, as well as a brief introduction to the machine learning techniques applicable to both economic questions and causal inference.