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.

The aim is to develop and extend current methodologies, such as Bayesian Causal Forests, for the estimation of heterogeneous treatment effects.