Machine Learning

Interpretable Deep Causal Learning for Moderation Effects

In this extended abstract paper, we address the problem of interpretability and targeted regularization in causal machine learning models. In particular, we focus on the problem of estimating individual causal/treatment effects under observed …

Shrinkage Bayesian Causal Forests for Heterogeneous Treatment Effects Estimation

This paper develops a sparsity-inducing version of Bayesian Causal Forests, a recently proposed nonparametric causal regression model that employs Bayesian Additive Regression Trees and is specifically designed to estimate heterogeneous treatment …

Estimating Individual Treatment Effects using Non-Parametric Regression Models: a Review

Large observational data are increasingly available in disciplines such as health, economic and social sciences, where researchers are interested in causal questions rather than prediction. In this paper, we investigate the problem of estimating …

Contributed Discussion of “Bayesian Regression Tree Models for Causal Inference: Regularization, Confounding, and Heterogeneous Effects”

Contributed discussion of the journal article “Bayesian Regression Tree Models for Causal Inference: Regularization, Confounding, and Heterogeneous Effects”, appeared in Bayesian Analysis, Vol. 15, Number 3 (2020), pp. 965-1056.