# Probit Monotone BART

## TL;DR

This paper introduces probit monotone BART, an extension of monotone BART, enabling it to estimate binary outcome functions within a Bayesian nonparametric framework.

## Contribution

It extends monotone BART to binary outcomes using a probit link, enhancing its applicability for classification tasks.

## Key findings

- Effective estimation of binary monotone functions.
- Improved accuracy over existing methods for binary data.
- Flexible nonparametric modeling of monotone relationships.

## Abstract

Bayesian Additive Regression Trees (BART) of Chipman et al. (2010) has proven to be a powerful tool for nonparametric modeling and prediction. Monotone BART (Chipman et al., 2022) is a recent development that allows BART to be more precise in estimating monotonic functions. We further these developments by proposing probit monotone BART, which allows the monotone BART framework to estimate conditional mean functions when the outcome variable is binary.