Bayesian Nonparametrics for Causal Inference

This project’s focus is on the use of Bayesian Nonparametric regression models for individual/heterogeneous treatment effects estimation and policy evaluation. In particular, the interest is in Bayesian Regression Trees methods (Bayesian Additive Regression Trees - BART), and their causal version, Bayesian Causal Forests.

Causal Machine Learning

In the recent years, there has been a surging interest in the use of Statistical/Machine Learning (ML) tools for causal inference. These tools can leverage large datasets and usually deliver excellent predictive performance.