mlsanityData to 7 Oct 2026

Generalized Bayesian Additive Regression Trees Models: Beyond Conditional Conjugacy

arXiv 2202.09924 ↗First posted 20 Feb 2022stat.ML cs.LG stat.MEcited by 8

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Abstract (arXiv)

Bayesian additive regression trees have seen increased interest in recent years due to their ability to combine machine learning techniques with principled uncertainty quantification. The Bayesian backfitting algorithm used to fit BART models, however, limits their application to a small class of models for which conditional conjugacy exists. In this article, we greatly expand the domain of applicability of BART to arbitrary generalized BART models by introducing a very simple, tuning-parameter-free, reversible jump Markov chain Monte Carlo algorithm. Our algorithm requires only that the user be able to compute the likelihood and (optionally) its gradient and Fisher information. The potential applications are very broad; we consider examples in survival analysis, structured heteroskedastic regression, and gamma shape regression.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 14 of its 48 references that are in the map. First posted on arXiv 20 Feb 2022.

Builds on

14 of 48 references.

References · 14

  1. 2017Automated versus do-it-yourself methods for causal inference: Lessons learned from a data analysis competitioncited by 106
  2. 2021Discussion on "Bayesian Regression Tree Models for Causal Inference: Regularization, Confounding, and Heterogeneous Effects" by Hahn, Murray and Carvalhocited by 77
  3. 2017Bayesian regression tree models for causal inference: regularization, confounding, and heterogeneous effectscited by 41
  4. 2017Bayesian Regression Tree Ensembles that Adapt to Smoothness and Sparsitycited by 34
  5. 2020Marginal likelihood computation for model selection and hypothesis testing: an extensive reviewcited by 33
  6. 2013bartMachine: Machine Learning with Bayesian Additive Regression Treescited by 24
  7. 2020Stochastic tree ensembles for regularized nonlinear regressioncited by 14
  8. 2017Individualized Treatment Effects with Censored Data via Fully Nonparametric Bayesian Accelerated Failure Time Modelscited by 13
  9. 2018Semiparametric Mixed-Scale Models Using Shared Bayesian Forestscited by 8
  10. 2020Adaptive Conditional Distribution Estimation with Bayesian Decision Tree Ensemblescited by 7
  11. 2021Density Regression with Bayesian Additive Regression Treescited by 5
  12. 2018XBART: Accelerated Bayesian Additive Regression Treescited by 4
2 more
  1. 2016Bayesian quantile additive regression treescited by 3
  2. 2018Fully Nonparametric Bayesian Additive Regression Treescited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 1; 2024: 3; 2025: 2; 2026: 1.

8 papers.

  1. 2022SoftBart: Soft Bayesian Additive Regression Treescited by 1
  2. 2025Horseshoe Forests for High-Dimensional Causal Survival Analysiscited by 1
  3. 2023Generalized Bayesian Additive Regression Trees: Theory and Softwarecited by 0
  4. 2024Bayesian Additive Regression Networkscited by 0
  5. 2024BARMPy: Bayesian Additive Regression Models Python Packagecited by 0
  6. 2024Heterogeneous Treatment Effect Estimation under Noncompliance in the Illinois Workplace Wellness Study with Bayesian Tree Ensemblescited by 0
  7. 2025Bayesian Neural Networks for Functional ANOVA modelcited by 0
  8. 2026AI4BayesCode: From Natural Language Descriptions to Validated Modular Stateful Bayesian Samplerscited by 0