Class notes: http://hedibert.org/wp-content/uploads/2018/06/BART.pdf Slides: Introduction to BART http://www.rob-mcculloch.org/BBC/BBC.pdf Bayesian Ensemble Learning http://www.rob-mcculloch.org/bart_asu-mlday_April-19-2019.pdf A General Approach to Variable Selection in Nonlinear Modelshttp://www.rob-mcculloch.org/chm/insper_varsel_sep-24-2020.pdf Causal Inference with the Instrumental Variable Approach and Bayesian Nonparametric Machine Learning http://www.rob-mcculloch.org/some_papers_and_talks/ivbart-talk_May-20-2021.pdf Two examples: Revisiting the ICU data: CART, BART and random forest (R code) http://hedibert.org/wp-content/uploads/2021/03/icu-glm-bayes-ridge-enet-lasso-cart-bart-rf.pdf http://hedibert.org/wp-content/uploads/2021/03/icu-glm-bayes-ridge-enet-lasso-cart-bart-rf-R.txt Revisiting Stock and Watson?s (2002) macro data http://hedibert.org/wp-content/uploads/2021/03/cart-bart-rf-stockwatson-R.txt A few researchers Robert McCulloch - http://www.rob-mcculloch.org/ Ed George - https://statistics.wharton.upenn.edu/profile/edgeorge/ Hugh Chipman - http://www.acadiau.ca/~hchipman/ Carlos Carvalho - https://faculty.mccombs.utexas.edu/carlos.carvalho/ Richard Hahn - https://math.la.asu.edu/~prhahn/ Antonio Linero - https://theodds.github.io/publications/ Jared Murray - https://jaredsmurray.github.io/ Matthew Pratola - http://www.matthewpratola.com/ Rodney Sparapani - https://www.mcw.edu/departments/biostatistics/people/rodney-sparapani-phd Jingyu He - https://jingyuhe.com/ Bibliography (chronological order) BART: BAYESIAN ADDITIVE REGRESSION TREES BY HUGH A. CHIPMAN, EDWARD I. GEORGE AND ROBERT E. MCCULLOCH The Annals of Applied Statistics 2010, Vol. 4, No. 1, 266?298 DOI: 10.1214/09-AOAS285 Nonparametric survival analysis using Bayesian Additive Regression Trees (BART) Rodney A Sparapani, Brent R Logan, Robert E McCulloch, Purushottam W Laud Stat Med. 2016 Jul 20;35(16):2741-53. DOI: 10.1002/sim.6893 A review of tree-based Bayesian methods Antonio Linero Communications for Statistical Applications and Methods 2017, Vol. 24, No. 6, 543–559 https://doi.org/10.29220/CSAM.2017.24.6.543 Decision making and uncertainty quantification for individualized treatments using Bayesian Additive Regression Trees Brent R Logan, Rodney Sparapani, Robert E McCulloch, Purushottam W Laud Statistical Methods in Medical Research, 2017, Volume: 28 issue: 4, page(s): 1079-1093 https://doi.org/10.1177/0962280217746191 Bayesian Regression Trees for High-Dimensional Prediction and Variable Selection Antonio R. Linero Journal of the American Statistical Association , Volume 113, 2018 - Issue 522 Pages 626-636 https://doi.org/10.1080/01621459.2016.1264957 Log-Linear Bayesian Additive Regression Trees for Multinomial Logistic and Count Responses Jared Murray Mon, 26 Aug 2019 https://arxiv.org/abs/1701.01503 XBART: Accelerated Bayesian Additive Regression Trees Jingyu He, Saar Yalov, P. Richard Hahn Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics, PMLR 89:1130-1138, 2019. http://proceedings.mlr.press/v89/he19a.html Nonparametric competing risks analysis using Bayesian Additive Regression Trees Rodney Sparapani, Brent R Logan, Robert E McCulloch, Purushottam W Laud Statistical Methods in Medical Research, 2019, Volume: 29 issue: 1, page(s): 57-77 https://doi.org/10.1177/0962280218822140 Bayesian additive regression trees and the General BART model Yaoyuan Vincent Tan, Jason Roy Statistics in Medicine, Volume 38, Issue 25, 10 November 2019. Pages 5048-5069 https://doi.org/10.1002/sim.8347 Fitting the fit, variable selection using surrogate models and decision analysis, a brief introduction and tutorial Carlos Carvalho, Richard P. Hahn and Robert McCulloch - April 21, 2020 http://www.rob-mcculloch.org/chm/nonlinvarsel.pdf Bayesian Additive Regression Trees: A Review and Look Forward Jennifer Hill,Antonio Linero, and Jared Murray Annual Review of Statistics and Its Application, Vol. 7:251-278 https://doi.org/10.1146/annurev-statistics-031219-041110 2020 BART with targeted smoothing: An analysis of patient-specific stillbirth risk Jennifer E. Starling, Jared S. Murray, Carlos M. Carvalho, Radek K. Bukowski, James G. Scott Ann. Appl. Stat. 14(1): 28-50 (March 2020). DOI: 10.1214/19-AOAS1268 Targeted Smooth Bayesian Causal Forests: An analysis of heterogeneous treatment effects for simultaneous versus interval medical abortion regimens over gestation Jennifer E. Starling, Jared S. Murray, Patricia A. Lohr, Abigail R.A. Aiken, Carlos M. Carvalho, James G. Scott https://arxiv.org/abs/1905.09405 - Sun, 23 Feb 2020 The Estimation of Causal Effects of Multiple Treatments in Observational Studies Using Bayesian Additive Regression Trees Chenyang Gu, Michael J. Lopez, Liangyuan Hu https://arxiv.org/abs/1901.04312 - 27 Feb 2020 Bayesian Regression Tree Models for Causal Inference: Regularization, Confounding, and Heterogeneous Effects (with Discussion) P. Richard Hahn, Jared S. Murray, Carlos M. Carvalho Bayesian Anal. 15(3): 965-1056 (September 2020). DOI: 10.1214/19-BA1195 Heteroscedastic BART via Multiplicative Regression Trees M. T. Pratola,H. A. Chipman,E. I. George &R. E. McCulloch Journal of Computational and Graphical Statistics Volume 29, 2020 - Issue 2 Pages 405-417 https://doi.org/10.1080/10618600.2019.1677243 mBART: Multidimensional Monotone BART Hugh A. Chipman, Edward I. George, Robert E. McCulloch and Thomas S. Shively Bayesian Analysis (2021) Causal Inference with the Instrumental Variable Approach and Bayesian Nonparametric Machine Learning Robert McCulloch ? Rodney Sparapani Brent Logan Purushottam Laud February 3, 2021 https://arxiv.org/pdf/2102.01199.pdf - February 3, 2021 Stochastic tree ensembles for regularized nonlinear regression Jingyu He and P. Richard Hahn Journal of the American Statistical Association Received 20 Mar 2020, Accepted 07 Jun 2021, Accepted author version posted online: 14 Jun 2021 https://doi.org/10.1080/01621459.2021.1942012 https://arxiv.org/abs/2002.03375 Bayesian and Empirical Bayes Forests Matt Taddy, Chun-Sheng Chen, Jun Yu and Mitch Wyle Proceedings of the 32 nd International Conference on Machine Learning, Lille, France, 2015. https://github.com/TaddyLab/bayesian-forest/blob/master/paper/bayesian-forests.pdf