Semiparametric mixed‐scale models using shared Bayesian forests

Linero, Antonio R.; Sinha, Debajyoti; Lipsitz, Stuart R.

doi:10.1111/biom.13107

Cited by 19 publications

(24 citation statements)

References 49 publications

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“…In future work, we believe that other BART-based models, such as SBART (Linero 2018), BART for Gamma and Log-normal hurdle data (Linero et al 2020), and log-linear BART (Murray 2021), could be extended to a semi-parametric approach. In addition, theoretical results underlying SP-BART could be developed in order to explore the properties of the proposed method in relation to the convergence of the posterior distribution.…”

Section: Discussionmentioning

confidence: 99%

Accounting for shared covariates in semi-parametric Bayesian additive regression trees

Prado¹,

Parnell²,

Murphy³

et al. 2021

Preprint

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We propose a new semi-parametric model based on Bayesian Additive Regression Trees (BART). In our approach, the response variable is approximated by a linear predictor and a BART model, where the first component is responsible for estimating the main effects and BART accounts for the non-specified interactions and non-linearities. The novelty in our approach lies in the way we change tree generation moves in BART to deal with confounding between the parametric and non-parametric components when they have covariates in common. Through synthetic and real-world examples, we demonstrate that the performance of the new semi-parametric BART is competitive when compared to regression models and other tree-based methods. The implementation of the proposed method is available at https://github.com/ebprado/SP-BART.

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Section: Discussionmentioning

confidence: 99%

Accounting for shared covariates in semi-parametric Bayesian additive regression trees

Prado¹,

Parnell²,

Murphy³

et al. 2021

Preprint

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“…A drawback of BART is that one usually needs to tailor it to the problem at hand. Since the initial work of Chipman et al (2010), which developed methods for semiparametric regression and classification, there have been substantial efforts to extend BART to other settings; a limited set of examples include survival analysis (Sparapani et al, 2016;Linero et al, 2021), Poisson regression (Murray, 2021), and gamma regression (Linero et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Generalized Bayesian Additive Regression Trees Models: Beyond Conditional Conjugacy

Linero¹

2022

Preprint

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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-parameterfree, 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.

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“…11 BART has also been extended to survival outcomes, 12,13 multinomial outcomes, 14,15 and semicontinuous outcomes. 16 In the causal inference literature, notable papers that promote the use of BART include those by Hill 5 and Green and Kern. 17 BART has also been consistently among the best performing methods in the Atlantic causal inference data analysis challenge.…”

Section: Introductionmentioning

confidence: 99%

Bayesian additive regression trees and the General BART model

Tan

Roy

2019

Statistics in Medicine

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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, and 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.

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Semiparametric mixed‐scale models using shared Bayesian forests

Cited by 19 publications

References 49 publications

Accounting for shared covariates in semi-parametric Bayesian additive regression trees

Accounting for shared covariates in semi-parametric Bayesian additive regression trees

Generalized Bayesian Additive Regression Trees Models: Beyond Conditional Conjugacy

Bayesian additive regression trees and the General BART model

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