Multinomial probit Bayesian additive regression trees

Kindo, Bereket P.; Wang, Hao; Peña, Edsel A.

doi:10.1002/sta4.110

Cited by 26 publications

(19 citation statements)

References 74 publications

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“…Specifically, this paper studies the "regularization-induced confounding" of Hahn et al (2016) in the context of Bayesian additive regression tree (BART) models as utilized by . In terms of implementation, this paper builds explicitly on the work of ; see also Gramacy and Lee (2008) and Murray (2017). Other notable work on Bayesian treatment effect estimation includes Gustafson and Greenland (2006), Zigler and Dominici (2014), Heckman et al (2014), Li and Tobias (2014), Roy et al (2017) and Taddy et al (2016).…”

Section: Relationship To Previous Literaturementioning

confidence: 99%

Bayesian Regression Tree Models for Causal Inference: Regularization, Confounding, and Heterogeneous Effects (with Discussion)

2020

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This paper presents a novel nonlinear regression model for estimating heterogeneous treatment effects, geared specifically towards situations with small effect sizes, heterogeneous effects, and strong confounding by observables. Standard nonlinear regression models, which may work quite well for prediction, have two notable weaknesses when used to estimate heterogeneous treatment effects. First, they can yield badly biased estimates of treatment effects when fit to data with strong confounding. The Bayesian causal forest model presented in this paper avoids this problem by directly incorporating an estimate of the propensity function in the specification of the response model, implicitly inducing a covariatedependent prior on the regression function. Second, standard approaches to response surface modeling do not provide adequate control over the strength of regularization over effect heterogeneity. The Bayesian causal forest model permits treatment effect heterogeneity to be regularized separately from the prognostic effect of control variables, making it possible to informatively "shrink to homogeneity". While we focus on observational data, our methods are equally useful for inferring heterogeneous treatment effects from randomized controlled experiments where careful regularization is somewhat less complicated but no less important. We illustrate these benefits via the reanalysis of an observational study assessing the causal effects of smoking on medical expenditures as well as extensive simulation studies.

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Section: Relationship To Previous Literaturementioning

confidence: 99%

Bayesian Regression Tree Models for Causal Inference: Regularization, Confounding, and Heterogeneous Effects (with Discussion)

2020

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“…BART's versatility has made it an attractive option with applications in credit risk modelling (Zhang and Härdle 2010), identification of subgroup effects in clinical trials (Sivaganesan et al 2017;Schnell et al 2016), competing risk analysis (Sparapani et al 2019), survival analysis of stem cell transplantation (Sparapani et al 2016), proteomic biomarker discovery (Hernández et al 2015) and causal inference (Hill 2011;Green and Kern 2012;Hahn et al 2020). In this context, many extensions have been proposed, such as BART for estimating monotone and smooth surfaces (Starling et al 2019(Starling et al , 2020Linero and Yang 2018), categorical and multinomial data (Murray 2017;Kindo et al 2016b), high-dimensional data (Hernández et al 2018;He et al 2019;Linero 2018), zeroinflated and semi-continuous responses , heterocedastic data (Pratola et al 2017) and BART with quantile regression and varying coefficient models (Kindo et al 2016a;Deshpande et al 2020). Recently, some papers have developed theoretical aspects related to BART (Linero 2017b;Ročková and van der Pas 2017;Ročková and Saha 2018;Linero and Yang 2018).…”

Section: Related Workmentioning

confidence: 99%

Bayesian additive regression trees with model trees

2021

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Bayesian additive regression trees (BART) is a tree-based machine learning method that has been successfully applied to regression and classification problems. BART assumes regularisation priors on a set of trees that work as weak learners and is very flexible for predicting in the presence of nonlinearity and high-order interactions. In this paper, we introduce an extension of BART, called model trees BART (MOTR-BART), that considers piecewise linear functions at node levels instead of piecewise constants. In MOTR-BART, rather than having a unique value at node level for the prediction, a linear predictor is estimated considering the covariates that have been used as the split variables in the corresponding tree. In our approach, local linearities are captured more efficiently and fewer trees are required to achieve equal or better performance than BART. Via simulation studies and real data applications, we compare MOTR-BART to its main competitors. R code for MOTR-BART implementation is available at https://github.com/ebprado/MOTR-BART.

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“…A detailed explanation of the BART model and its different components associated with this modeling approach can be found in Kapelner and Bleich [30]. BART can be configured for the classification problem with binary outcomes [16] and multiple classes as well [31]. In this analysis, the BART modeling technique is used for the same dataset as considered for RF models to compare the performance of these two ML techniques.…”

Section: Bayesian Additive Regression Trees (Bart)mentioning

confidence: 99%

Advancement of weather-related crash prediction model using nonparametric machine learning algorithms

2020

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This paper evaluates the machine learning-based weather-related crash prediction model in Connecticut. Crash severity prediction has always been the principal focus of safety professionals and emergency responders for appropriate policy making and resource management. Over the years, different statistical methodologies (e.g., random forest, support vector machine) have been explored in various research efforts to develop efficient crash severity prediction models. As technology is advancing and computing has started becoming more efficient, machine learning-based models for crash severity prediction are being brought into light for more accurate data-driven prediction. However, some machine learning methodologies provide increased efficiency and better performance compared to others from the same genre. To explore different machine learning methodologies for crash severity prediction, this study considered two machine learning applications-random forest (RF) and bayesian additive regression trees (BART) for performance comparison. RF model produced higher prediction probabilities for determining crash severity compare to BART. The results were evaluated using various prediction probability analyses obtained from these two machine learning models and showed the model capabilities to generate prediction consistent with the observed data. We found the performance of the RF model to be highly promising with a higher skill score (0.73) than BART (0.61). Overall, our findings demonstrate the robust performances of the RF algorithm in predicting weather-related crashes. The analysis of this study confirms that stakeholders can use weather-related RF model with confidence to obtain a better prediction of crash severity that enables them to facilitate appropriate emergency responses and support essential preemptive measures.

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Multinomial probit Bayesian additive regression trees

Cited by 26 publications

References 74 publications

Bayesian Regression Tree Models for Causal Inference: Regularization, Confounding, and Heterogeneous Effects (with Discussion)

Bayesian Regression Tree Models for Causal Inference: Regularization, Confounding, and Heterogeneous Effects (with Discussion)

Bayesian additive regression trees with model trees

Advancement of weather-related crash prediction model using nonparametric machine learning algorithms

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