Work

bartcs: An R Package for Bayesian Nonparametric Adjustment for Confounding

Year: 2025

Type: article

Abstract: This article presents an overview of the bartcs R package, which employs a Bayesian additive regression trees-based method for selecting confounders. It uses a Dirichlet distribution as a common variable selection probability prior, updating both the exposure and outcome models simultaneously while fitting tree priors for each. This data-driven method determines which variables (i.e., confounders) affect both models by assigning more posterior weight to them. It supports continuous and binary exposure variables, as well as continuous outcome variables, and is written in C++ for improved computational speed. Additionally, it can take advantage of multiple threads for parallel computing if OpenMP is available on the platform.

Source: The R Journal

DOI: 10.32614/rj-2025-019

Authors: Yeonghoon Yoo, Chanmin Kim (corresponding author)

Institution: Sungkyunkwan University

Language: English

Topic: Advanced Causal Inference Techniques

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: diamond

Funders: National Research Foundation, National Research Foundation of Korea

Awards: G3907894604, G3986888107, G8555631934