A semiparametric modeling approach using Bayesian Additive Regression Trees with an application to evaluate heterogeneous treatment effects

TL;DR

This paper introduces a semiparametric Bayesian regression framework combining BART with linear models to effectively analyze treatment effects while controlling for confounders, demonstrated through simulations and a clinical dataset.

Contribution

It extends BART to a semiparametric model that separates variables of scientific interest from nuisance variables, enabling flexible causal inference with interpretability.

Findings

Effective in capturing nonlinearities and interactions.

Applicable to causal modeling with structural mean models.

Validated through simulations and real-world HIV/Hepatitis C data.

Abstract

Bayesian Additive Regression Trees (BART) is a flexible machine learning algorithm capable of capturing nonlinearities between an outcome and covariates and interaction among covariates. We extend BART to a semiparametric regression framework in which the conditional expectation of an outcome is a function of treatment, its effect modifiers, and confounders. The confounders, not of scientific interest, are allowed to have unspecified functional form, while treatment and other covariates that do have scientific importance are given the usual linear form from parametric regression. The result is a Bayesian semiparametric linear regression model where the posterior distribution of the parameters of the linear part can be interpreted as in parametric Bayesian regression. This is useful in situations where a subset of the variables are of substantive interest and the others are nuisance variables that we would like to control for. An example of this occurs in causal modeling with the structural mean model (SMM). Under certain causal assumptions, our method can be used as a Bayesian SMM. Our methods are demonstrated with simulation studies and an application to dataset involving adults with HIV/Hepatitis C coinfection who newly initiate antiretroviral therapy. The methods are available in an R package semibart.