Causal Machine Learning

Undergraduate / Graduate

By the end of this course students should understand the recent research and methods in statistical inference after machine learning. Chiefly, this course focuses on causal inference, but other topics are covered as well. The course aims for a theoretical understanding and as well as ready-to-deploy tools. Students will be introduced to the theoretical underpinnings of this material, which includes studying topics in nonparametric estimation, two-step and semiparametric inference theory, including the special case of double/debiased machine learning. Methods discussed include neural networks, random forests, and LASSO estimation.