Can I read Shrinkage Bayesian Causal Forests for Heterogeneous Treatment Effects Estimation on EtoBox?

Shrinkage Bayesian Causal Forests for Heterogeneous Treatment Effects Estimation by Caron, Alberto; Baio, Gianluca; Manolopoulou, Ioanna is a scholarly article available to read on EtoBox.

What is Shrinkage Bayesian Causal Forests for Heterogeneous Treatment Effects Estimation about?

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 effects using observational data. The sparsity-inducing component we introduce is motivated by empirical studies where not all the available covariates are relevant, leading to different degrees of sparsity underlying the surfaces of interest in the estimation of individual treatment effects. The extended version presented in this work, which we name Shrinkage Bayesian Causal Forest, is equipped with an additional pair of priors allowing the model to adjust the weight of each covariate through the corresponding number of splits in the tree ensemble. These priors improve the model's adaptability to sparse data generating processes and allow to perform fully Bayesian feature shrinkage in a framework for treatment effects estimation, and thus to uncover the moderating factors driving heterogeneity. In addition, the method allows prior knowledge about the relevant confounding covariates and the relative magnitude of their impact on the outcome

Author

Caron, Alberto; Baio, Gianluca; Manolopoulou, Ioanna

Published

2021

Language

EN