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bartMachine: Machine Learning with Bayesian Additive Regression Trees

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  • Kapelner, Adam
  • Bleich, Justin

Abstract

We present a new package in R implementing Bayesian additive regression trees (BART). The package introduces many new features for data analysis using BART such as variable selection, interaction detection, model diagnostic plots, incorporation of missing data and the ability to save trees for future prediction. It is significantly faster than the current R implementation, parallelized, and capable of handling both large sample sizes and high-dimensional data.

Suggested Citation

  • Kapelner, Adam & Bleich, Justin, 2016. "bartMachine: Machine Learning with Bayesian Additive Regression Trees," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 70(i04).
    • Handle: RePEc:jss:jstsof:v:070:i04
    DOI: http://hdl.handle.net/10.18637/jss.v070.i04
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    Cited by:

    1. Jan Kluge & Sarah Lappöhn & Kerstin Plank, 2023. "Predictors of TFP growth in European countries," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 50(1), pages 109-140, February.
    2. Ganguly, Prasangsha & Mukherjee, Sayanti, 2021. "A multifaceted risk assessment approach using statistical learning to evaluate socio-environmental factors associated with regional felony and misdemeanor rates," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 574(C).
    3. Chanmin Kim & Mauricio Tec & Corwin Zigler, 2023. "Bayesian nonparametric adjustment of confounding," Biometrics, The International Biometric Society, vol. 79(4), pages 3252-3265, December.
    4. Falco J. Bargagli-Stoffi & Fabio Incerti & Massimo Riccaboni & Armando Rungi, 2023. "Machine Learning for Zombie Hunting: Predicting Distress from Firms' Accounts and Missing Values," Papers 2306.08165, arXiv.org.
    5. Lihua Lei & Emmanuel J. Candès, 2021. "Conformal inference of counterfactuals and individual treatment effects," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(5), pages 911-938, November.
    6. Benjamin Küfner & Joseph W. Sakshaug & Stefan Zins, 2022. "Analysing establishment survey non‐response using administrative data and machine learning," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(S2), pages 310-342, December.
    7. Alpha Forna & Ilaria Dorigatti & Pierre Nouvellet & Christl A Donnelly, 2021. "Comparison of machine learning methods for estimating case fatality ratios: An Ebola outbreak simulation study," PLOS ONE, Public Library of Science, vol. 16(9), pages 1-15, September.
    8. Pierdzioch, Christian & Risse, Marian & Rohloff, Sebastian, 2016. "Are precious metals a hedge against exchange-rate movements? An empirical exploration using bayesian additive regression trees," The North American Journal of Economics and Finance, Elsevier, vol. 38(C), pages 27-38.
    9. Kristina Blennow & Johannes Persson, 2021. "To Mitigate or Adapt? Explaining Why Citizens Responding to Climate Change Favour the Former," Land, MDPI, vol. 10(3), pages 1-13, March.
    10. Pierdzioch, Christian & Risse, Marian & Gupta, Rangan & Nyakabawo, Wendy, 2019. "On REIT returns and (un-)expected inflation: Empirical evidence based on Bayesian additive regression trees," Finance Research Letters, Elsevier, vol. 30(C), pages 160-169.
    11. Falco J. Bargagli Stoffi & Kenneth De Beckker & Joana E. Maldonado & Kristof De Witte, 2021. "Assessing Sensitivity of Machine Learning Predictions.A Novel Toolbox with an Application to Financial Literacy," Papers 2102.04382, arXiv.org.
    12. Huaiyu Zang & Hang J. Kim & Bin Huang & Rhonda Szczesniak, 2023. "Bayesian causal inference for observational studies with missingness in covariates and outcomes," Biometrics, The International Biometric Society, vol. 79(4), pages 3624-3636, December.
    13. Lamprinakou, Stamatina & Barahona, Mauricio & Flaxman, Seth & Filippi, Sarah & Gandy, Axel & McCoy, Emma J., 2023. "BART-based inference for Poisson processes," Computational Statistics & Data Analysis, Elsevier, vol. 180(C).
    14. Kristina Blennow & Erik Persson & Johannes Persson, 2021. "DeveLoP—A Rationale and Toolbox for Democratic Landscape Planning," Sustainability, MDPI, vol. 13(21), pages 1-20, November.
    15. Jenny Häggström, 2018. "Data†driven confounder selection via Markov and Bayesian networks," Biometrics, The International Biometric Society, vol. 74(2), pages 389-398, June.
    16. Martin Huber & David Imhof & Rieko Ishii, 2022. "Transnational machine learning with screens for flagging bid‐rigging cartels," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(3), pages 1074-1114, July.
    17. Dehghani, Nariman L. & Zamanian, Soroush & Shafieezadeh, Abdollah, 2021. "Adaptive network reliability analysis: Methodology and applications to power grid," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    18. Kluge, Jan & Lappoehn, Sarah & Plank, Kerstin, 2020. "The Determinants of Economic Competitiveness," IHS Working Paper Series 24, Institute for Advanced Studies.
    19. Bryan Keller, 2020. "Variable Selection for Causal Effect Estimation: Nonparametric Conditional Independence Testing With Random Forests," Journal of Educational and Behavioral Statistics, , vol. 45(2), pages 119-142, April.

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