# SoftBart: Soft Bayesian Additive Regression Trees

arXiv: https://arxiv.org/abs/2210.16375 · HTML: https://mlsanity.com/paper/2210.16375/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 49 references that are in the map.
- First posted on arXiv 28 Oct 2022.

## Builds on (most cited first, 13 of 13)

- [Discussion on "Bayesian Regression Tree Models for Causal Inference: Regularization, Confounding, and Heterogeneous Effects" by Hahn, Murray and Carvalho](https://mlsanity.com/paper/2108.02836/) (2021)
- [Bayesian Regression Tree Ensembles that Adapt to Smoothness and Sparsity](https://mlsanity.com/paper/1707.09461/) (2017)
- [bartMachine: Machine Learning with Bayesian Additive Regression Trees](https://mlsanity.com/paper/1312.2171/) (2013)
- [Individualized Treatment Effects with Censored Data via Fully Nonparametric Bayesian Accelerated Failure Time Models](https://mlsanity.com/paper/1706.06611/) (2017)
- [Bayesian Additive Regression Trees with Model Trees](https://mlsanity.com/paper/2006.07493/) (2020)
- [Semiparametric Mixed-Scale Models Using Shared Bayesian Forests](https://mlsanity.com/paper/1809.08521/) (2018)
- [Generalized Bayesian Additive Regression Trees Models: Beyond Conditional Conjugacy](https://mlsanity.com/paper/2202.09924/) (2022)
- [VCBART: Bayesian trees for varying coefficients](https://mlsanity.com/paper/2003.06416/) (2020)
- [Adaptive Conditional Distribution Estimation with Bayesian Decision Tree Ensembles](https://mlsanity.com/paper/2005.02490/) (2020)
- [Density Regression with Bayesian Additive Regression Trees](https://mlsanity.com/paper/2112.12259/) (2021)
- [Bayesian Additive Regression Trees With Parametric Models of Heteroskedasticity](https://mlsanity.com/paper/1402.5397/) (2014)
- [BART-based inference for Poisson processes](https://mlsanity.com/paper/2005.07927/) (2020)
- [Inference from Non-Random Samples Using Bayesian Machine Learning](https://mlsanity.com/paper/2104.05192/) (2021)

## Built on by (most cited first, 2 of 2)

- [Bayesian Mediation Analysis for Individualized Treatment Rules](https://mlsanity.com/paper/2607.28804/) (2026)
- [Calibrated Bayesian Inference for Stochastic Intervention Effects](https://mlsanity.com/paper/2608.02924/) (2026)

## Abstract (arXiv)

Bayesian additive regression tree (BART) models have seen increased attention in recent years as a general-purpose nonparametric modeling technique. BART combines the flexibility of modern machine learning techniques with the principled uncertainty quantification of Bayesian inference, and it has been shown to be uniquely appropriate for addressing the high-noise problems that occur commonly in many areas of science, including medicine and the social sciences. This paper introduces the SoftBart package for fitting the Soft BART algorithm of Linero and Yang (2018). In addition to improving upon the predictive performance of other BART packages, a major goal of this package has been to facilitate the inclusion of BART in larger models, making it ideal for researchers in Bayesian statistics. I show both how to use this package for standard prediction tasks and how to embed BART models in larger models; I illustrate by using SoftBart to implement a nonparametric probit regression model, a semiparametric varying coefficient model, and a partial linear model.