Abstract Bayesian Causal Forests (BCF) is a causal inference machine learning model
based on the flexible non-parametric regression and classification tool, Bayesian Additive …

We demonstrate how Hahn et al.'s Bayesian Causal Forests model (BCF) can be used to
estimate conditional average treatment effects for the longitudinal dataset in the 2022 …

This article develops a sparsity-inducing version of Bayesian Causal Forests, a recently
proposed nonparametric causal regression model that employs Bayesian Additive …

Determining subgroups that respond especially well (or poorly) to specific interventions
(medical or policy) requires new supervised learning methods tailored specifically for causal …

There is a growing interest in using machine learning (ML) methods for causal inference due
to their (nearly) automatic and flexible ability to model key quantities such as the propensity …

Latent confounders—unobserved variables that influence both treatment and outcome—can
bias estimates of causal effects. In some cases, these confounders are shared across …

We propose some extensions to semi-parametric models based on Bayesian additive
regression trees (BART). In the semi-parametric BART paradigm, the response variable is …

The inclusion of the propensity score as a covariate in Bayesian regression trees for causal
inference can reduce the bias in treatment effect estimations, which occurs due to the …

Recently, there has been growing interest in using machine learning methods for causal
inference due to their automatic and flexible ability to model the propensity score and the …

This paper conducts a methodological review of papers using the causal forest machine
learning method for flexibly estimating heterogeneous treatment effects. It examines 133 …