# Bayesian additive regression trees and the General BART model

## TL;DR

This paper provides a comprehensive tutorial on Bayesian additive regression trees (BART), explaining its components, and introduces the General BART framework that unifies various recent extensions for diverse research applications.

## Contribution

It offers a detailed explanation of BART and introduces the General BART model, unifying multiple recent extensions for broader applicability.

## Key findings

- Clarifies the components and functioning of BART.
- Introduces the General BART framework for diverse models.
- Demonstrates how to apply BART to complex research problems.

## Abstract

Bayesian additive regression trees (BART) is a flexible prediction model/machine learning approach that has gained widespread popularity in recent years. As BART becomes more mainstream, there is an increased need for a paper that walks readers through the details of BART, from what it is to why it works. This tutorial is aimed at providing such a resource. In addition to explaining the different components of BART using simple examples, we also discuss a framework, the General BART model, that unifies some of the recent BART extensions, including semiparametric models, correlated outcomes, statistical matching problems in surveys, and models with weaker distributional assumptions. By showing how these models fit into a single framework, we hope to demonstrate a simple way of applying BART to research problems that go beyond the original independent continuous or binary outcomes framework.