Shrinkage Bayesian Causal Forests for Heterogeneous Treatment Effects Estimation
用于异质性处理效应估计的收缩贝叶斯因果森林
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…