Shrinkage Bayesian Causal Forests for Heterogeneous Treatment Effects Estimation*

@inproceedings{Caron2022ShrinkageBC,
  title={Shrinkage Bayesian Causal Forests for Heterogeneous Treatment Effects Estimation*},
  author={Alberto Caron and Gianluca Baio and Ioanna Manolopoulou},
  year={2022}
}
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… 

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