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.

Alberto Caron
Alberto Caron
PhD Candidate in Statistics & Machine Learning

Alberto Caron, second-year PhD Student in Statistics & Machine Learning at the Department of Statistical Science, University College London