Optimal Prescriptive Trees

Published Online:https://doi.org/10.1287/ijoo.2018.0005

References

  • Athey S, Imbens G (2016) Recursive partitioning for heterogeneous causal effects. Proc. Natl. Acad. Sci. USA 113(27):7353–7360.Google Scholar
  • Baron G, Perrodeau E, Boutron I, Ravaud P (2013) Reporting of analyses from randomized controlled trials with multiple arms: A systematic review. BMC Medicine 11(1):84.Google Scholar
  • Bastani H, Bayati M (2015) Online decision-making with high-dimensional covariates. Working paper, Stanford University, Palo Alto, CA.Google Scholar
  • Bennett KP, Blue J (1996) Optimal decision trees. Math Report No. 214, Rensselaer Polytechnic Institute, Troy, NY.Google Scholar
  • Bertsimas D, Dunn J (2017) Optimal classification trees. Machine Learn. 106(7):1039–1082.Google Scholar
  • Bertsimas D, Dunn J (2019) Machine Learning Under a Modern Optimization Lens (Dynamic Ideas, Belmont, MA).Google Scholar
  • Bertsimas D, Kallus N (2016) Pricing from observational data. Working paper, Massachusetts Institute of Technology, Cambridge.Google Scholar
  • Bertsimas D, Kallus N (2019) From predictive to prescriptive analytics. Management Sci. Forthcoming.Google Scholar
  • Bertsimas D, Kallus N, Weinstein A, Zhuo YD (2017) Personalized diabetes management using electronic medical records. Diabetes Care 40(2):210–217.Google Scholar
  • Breiman L (2001a) Random forests. Machine Learn. 45(1):5–32.Google Scholar
  • Breiman L (2001b) Statistical modeling: The two cultures (with comments and rejoinder by the author). Statist. Sci. 16(3):199–231.Google Scholar
  • Breiman L, Friedman J, Olshen R, Stone C (1984) Classification and Regression Trees (Wadsworth and Brooks, Monterey, CA).Google Scholar
  • Buffery D (2015) The 2015 oncology drug pipeline: Innovation drives the race to cure cancer. Amer. Health Drug Benefits 8(4):216–222.Google Scholar
  • Dunn J (2018) Optimal trees for prediction and prescription. PhD thesis, Massachusetts Institute of Technology, Cambridge.Google Scholar
  • Feldstein ML, Savlov ED, Hilf R (1978) A statistical model for predicting response of breast cancer patients to cytotoxic chemotherapy. Cancer Res. 38(8):2544–2548.Google Scholar
  • Flume PA, O’Sullivan BP, Robinson KA, Goss CH, Mogayzel PJ Jr, Willey-Courand DB, Bujan J, Finder J, Lester M, Quittell L (2007) Cystic fibrosis pulmonary guidelines: Chronic medications for maintenance of lung health. Amer. J. Respiratory Critical Care Medicine 176(10):957–969.Google Scholar
  • Gittins JC (1989) Multi-Armed Bandit Allocation Indices (Wiley, Chichester, UK).Google Scholar
  • Goldenshluger A, Zeevi A (2013) A linear response bandit problem. Stochastic Systems 3(1):230–261.LinkGoogle Scholar
  • Gort M, Broekhuis M, Otter R, Klazinga NS (2007) Improvement of best practice in early breast cancer: Actionable surgeon and hospital factors. Breast Cancer Res. Treatment 102(2):219–226.Google Scholar
  • Grubinger T, Zeileis A, Pfeiffer K-P (2014) evtree: Evolutionary learning of globally optimal classification and regression trees in R. J. Statist. Software 61(1):1–29.Google Scholar
  • Hill JL (2011) Bayesian nonparametric modeling for causal inference. J. Comput. Graphical Statist. 20(1):217–240.Google Scholar
  • Imai K, Ratkovic M (2014) Covariate balancing propensity score. J. Royal Statist. Soc. Ser. B Statist. Methodology 76(1):243–263.Google Scholar
  • Insel TR (2009) Translating scientific opportunity into public health impact: A strategic plan for research on mental illness. Arch. General Psychiatry 66(2):128–133.Google Scholar
  • International Warfarin Pharmacogenetics Consortium, et al.. (2009) Estimation of the warfarin dose with clinical and pharmacogenetic data. New England J. Medicine 2009(360):753–764.Google Scholar
  • Jaffer A, Bragg L (2003) Practical tips for warfarin dosing and monitoring. Cleveland Clinic J. Medicine 70(4):361–371.Google Scholar
  • Kallus N (2017) Recursive partitioning for personalization using observational data. Proc. 34th Internat. Conf. Machine Learn., Sydney, Australia, 1789–1798.Google Scholar
  • LaLonde RJ (1986) Evaluating the econometric evaluations of training programs with experimental data. Amer. Econom. Rev. 76(4):604–620.Google Scholar
  • Li L, Chu W, Langford J, Schapire RE (2010) A contextual-bandit approach to personalized news article recommendation. Proc. 19th Internat. Conf. World Wide Web (ACM, New York), 661–670.Google Scholar
  • Lipkovich I, Dmitrienko A (2014) Strategies for identifying predictive biomarkers and subgroups with enhanced treatment effect in clinical trials using sides. J. Biopharmaceutical Statist. 24(1):130–153.Google Scholar
  • Morgan SL, Winship C (2014) Counterfactuals and Causal Inference (Cambridge University Press, Cambridge, UK).Google Scholar
  • Parmar MKB, Carpenter J, Sydes MR (2014) More multiarm randomised trials of superiority are needed. Lancet 384(9940):283–284.Google Scholar
  • Powers S, Qian J, Jung K, Schuler A, Nigam HS, Hastie T, Tibshirani R (2017) Some methods for heterogenous treatment effect estimation in high dimensions. Working paper, Stanford University, Palo Alto, CA.Google Scholar
  • Qian M, Murphy SA (2011) Performance guarantees for individualized treatment rules. Ann. Statist. 39(2):1180.Google Scholar
  • Rosenbaum PR, Rubin DB (1983) The central role of the propensity score in observational studies for causal effects. Biometrika 70(1):41–55.Google Scholar
  • Son NH (1998) From optimal hyperplanes to optimal decision trees. Fundamenta Informaticae 34(1, 2):145–174.Google Scholar
  • Wager S, Athey S (2018) Estimation and inference of heterogeneous treatment effects using random forests. J. Amer. Statist. Assoc. 113(523):1228–1242.Google Scholar
  • Westreich D, Lessler J, Funk MJ (2010) Propensity score estimation: Machine learning and classification methods as alternatives to logistic regression. J. Clinical Epidemiology 63(8):826–833.Google Scholar
  • Zhou X, Mayer-Hamblett N, Khan U, Kosorok MR (2017) Residual weighted learning for estimating individualized treatment rules. J. Amer. Statist. Assoc. 112(517):169–187.Google Scholar
  • Zou H, Hastie T (2005) Regularization and variable selection via the elastic net. J. Royal Statist. Soc. Ser. B Statist. Methodology 67(2):301–320.Google Scholar
  • Zubizarreta JR (2012) Using mixed integer programming for matching in an observational study of kidney failure after surgery. J. Amer. Statist. Assoc. 107(500):1360–1371.Google Scholar
INFORMS site uses cookies to store information on your computer. Some are essential to make our site work; Others help us improve the user experience. By using this site, you consent to the placement of these cookies. Please read our Privacy Statement to learn more.