Experimental Evaluation of Individualized Treatment Rules

References

• Andrews, I., Kitagawa, T., and McCloskey, A. (2020), “Inference on Winners,” Tech. rep., Harvard University, Department of Economics.
   Google Scholar
• Ascarza, E. (2018), “Retention Futility: Targeting High-Risk Customers Might be Ineffective,” Journal of Marketing Research, 55, 1, 80–98. DOI: 10.1509/jmr.16.0163.
   Web of Science ®Google Scholar
• Athey, S., and Imbens, G. (2016), “Recursive Partitioning for Heterogeneous Causal Effects,” Proceedings of the National Academy of Sciences, pp. 7353–7360. DOI: 10.1073/pnas.1510489113.
   PubMed Web of Science ®Google Scholar
• Athey, S., Tibshirani, J., Wager, S. (2019), “Generalized Random Forests,” Annals of Statistics 47, 1148–1178.
   Web of Science ®Google Scholar
• Athey, S., and Wager, S. (2018), “Efficient Policy Learning,” arXiv no. 1702.02896 .
   Google Scholar
• Chakraborty, B., Laber, E., and Zhao, Y.-Q. (2014), “Inference about the Expected Performance of a Data-Driven Dynamic Treatment Regime,” Clinical Trials, 11, 408–417. DOI: 10.1177/1740774514537727.
   PubMed Web of Science ®Google Scholar
• Chernozhukov, V., Demirer, M., Duflo, E., and Fernandez-Val, I. (2019), “Generic Machine Learning Inference on Heterogeneous Treatment Effects in Randomized Experiments,” Tech. rep.
   Google Scholar
• Chipman, H. A., George, E. I., McCulloch, R. E. (2010), “Bart: Bayesian Additive Regression Trees,” The Annals of Applied Statistics 4, 266–298. DOI: 10.1214/09-AOAS285.
   Web of Science ®Google Scholar
• Diemert, E., Betlei, A., Renaudin, C., and Amini, M.-R. (2018), “A Large Scale Benchmark for Uplift Modeling,” In KDD, London, UK.
   Google Scholar
• Ding, W., and Lehrer, S. F. (2011), “Experimental Estimates of the Impacts of Class Size on Test Scores: Robustness and Heterogeneity,” Education Economics 19, 229–252. DOI: 10.1080/09645292.2011.589142.
   Google Scholar
• Dudík, M., Langford, J., and Li, L. (2011), “Doubly Robust Policy Evaluation and Learning,” in Proceedings of the 28th International Conference on International Conference on Machine Learning (ICML’11), Marina Vannucci, ed., Rice University, Noah Harding, Omnipress. pp. 1097–1104.
   Google Scholar
• Fifield, B. (2018), “Empirical and Computational Methods for Electoral Politics,” Ph.D. thesis, Princeton University.
   Google Scholar
• Finn, J. D., and Achilles, C. M. (1990), “Answers and Questions About Class Size: A Statewide Experiment,” American Educational Research Journal, 27, 557–577. DOI: 10.3102/00028312027003557.
   Web of Science ®Google Scholar
• Fu, H., Zhou, J., and Faries, D. E. (2016), “Estimating Optimal Treatment Regimes via Subgroup Identification in Randomized Control Trials and Observational Studies,” Statistics in Medicine, 35, 19, 3285–3302. DOI: 10.1002/sim.6920.
   PubMed Web of Science ®Google Scholar
• Grimmer, J., Messing, S., and Westwood, S. J. (2017), “Estimating Heterogeneous Treatment Effects and The Effects of Heterogeneous Treatments With Ensemble Methods,” Political Analysis, 25, 413–434. DOI: 10.1017/pan.2017.15.
   Web of Science ®Google Scholar
• Gutierrez, P., and Gérardy, J.-Y. (2016). “Causal Inference and Uplift Modelling: A Review of the Literature,” in Proceedings of Machine Learning Research, International Conference on Predictive Applications and APIs, Boston, MA, Vol. 67, pp. 1–13.
   Google Scholar
• Hahn, P. R., Dorie, V., and Murray, J. S. (2018), “Atlantic Causal Inference Conference (ACIC) Data Analysis Challenge 2017,” Tech. rep., School of Mathematical and Statistical Sciences.
   Google Scholar
• Hahn, P. R., Murray, J. S., Carvalho, C. M. (2020), “Bayesian Regression Tree Models for Causal Inference: Regularization, Confounding, and Heterogeneous Effects,” Bayesian Analysis, 15, 965–1056. DOI: 10.1214/19-BA1195.
   Google Scholar
• Hamburg, M. A., and Collins, F. S. (2010), “The Path to Personalized Medicine,” New England Journal of Medicine, 363, 301–304. DOI: 10.1056/NEJMp1006304.
   PubMed Web of Science ®Google Scholar
• Imai, K., and Ratkovic, M. (2013), “Estimating Treatment Effect Heterogeneity in Randomized Program Evaluation,” Annals of Applied Statistics, 7, 443–470.
   Web of Science ®Google Scholar
• Imai, K., and Strauss, A. (2011), “Estimation of Heterogeneous Treatment Effects from Randomized Experiments, with Application to the Optimal Planning of the Get-Out-the-Vote Campaign,” Political Analysis, 19, 1–19. DOI: 10.1093/pan/mpq035.
   Web of Science ®Google Scholar
• Imbens, G. W., and Rubin, D. B. (2015), Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction, Cambridge: Cambridge University Press.
   Google Scholar
• Jackson, E., and Page, M. E. (2013), “Estimating the Distributional Effects of Education Reforms: A Look at Project Star,” Economics of Education Review, 32, 92–103. DOI: 10.1016/j.econedurev.2012.07.017.
   Web of Science ®Google Scholar
• Janitza, S., and Hornung, R. (2018), “On the Overestimation of Random Forest’s Out-of-Bag Error,” PLoS ONE 13, 8, e0201904. DOI: 10.1371/journal.pone.0201904.
   PubMed Web of Science ®Google Scholar
• Jiang, N., and Li, L. (2016), “Doubly Robust Off-Policy Value Evaluation for Reinforcement Learning,” in Proceedings of The 33rd International Conference on Machine Learning, vol. 48 of Proceedings of Machine Learning Research, pp. 652–661.
   Google Scholar
• Kallus, N. (2018), “Balanced Policy Evaluation and Learning,” in Advances in Neural Information Processing Systems (Vol. 31), eds. S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, and R. Garnett. Curran Associates, Inc. pp. 8895–8906
   Google Scholar
• Kamath, P. S., Wiesner, R. H., Malinchoc, M., Kremers, W., Therneau, T. M., Kosberg, C. L., D’Amico, G., Dickson, E. R., and Kim, W. R. (2001), “A Model to Predict Survival in Patients with End-Stage Liver Disease,” Hepatology, 33, 464–470. DOI: 10.1053/jhep.2001.22172.
   PubMed Web of Science ®Google Scholar
• Kitagawa, T., and Tetenov, A. (2018), “Who Should Be Treated?: Empirical Welfare Maximization Methods for Treatment Choice,” Econometrica, 86, 591–616. DOI: 10.3982/ECTA13288.
   Web of Science ®Google Scholar
• Künzel, S. R., Sekhon, J. S., Bickel, P. J., and Yu, B. (2019), “Meta-Learners for Estimating Heterogeneous Treatment Effects Using Machine Learning,” Proceedings of the National Academy of Sciences, 116, 4156–4165. DOI: 10.1073/pnas.1804597116.
   PubMed Web of Science ®Google Scholar
• Luedtke, A. R., and van der Laan, M. J. (2016a), “Optimal Individualized Treatments in Resource-Limited Settings,” International Journal of Biostatistics, 12, 283–303. DOI: 10.1515/ijb-2015-0007.
   Web of Science ®Google Scholar
• Luedtke, A. R., and van der Laan, M. J. (2016b), “Statistical Inference for the Mean Outcome Under a Possibly Non-Unique Optimal Treatment Strategy,” Annals of Statistics, 44, 2, 713–742.
   PubMed Web of Science ®Google Scholar
• Manski, C. F. (2004), “Statistical Treatment Rules for Heterogeneous Populations,” Econometrica, 72, 1221–1246. DOI: 10.1111/j.1468-0262.2004.00530.x.
   Web of Science ®Google Scholar
• McKee, G., Sims, K. R. E., and Rivkin, S. G. (2015), “Disruption, Learning, and The Heterogeneous Benefits of Smaller Classes,” Empirical Economics, 48, 1267–1286. DOI: 10.1007/s00181-014-0810-1.
   Web of Science ®Google Scholar
• Mosteller, F. (1995), “The Tennessee Study of Class Size in The Early School Grades,” The Future of Children, 5, 113–127. DOI: 10.2307/1602360.
   PubMed Web of Science ®Google Scholar
• Neyman, J. (1923), “On the Application of Probability Theory to Agricultural Experiments: Essay on Principles, Section 9. (translated in 1990),” Statistical Science, 5, 465–480.
   Google Scholar
• Nye, B. A., Hedges, L. V., and Konstantopoulos, S. (2000), “Do The Disadvantaged Benefit More from Small Classes? Evidence from The Tennessee Class Size Experiment,” American Journal of Education, 109, 1–26. DOI: 10.1086/444257.
   Web of Science ®Google Scholar
• Qian, M., and Murphy, S. A. (2011), “Performance Gurantees for Individualized Treatment Rules,” Annals of Statistics, 39, 1180–1210.
   PubMed Web of Science ®Google Scholar
• Radcliffe, N. J. (2007), “Using Control Groups to Target on Predicted Lift: Building and Assessing Uplift Models,” Direct Marketing Analytics Journal 1, 14–21.
   Google Scholar
• Rai, Y. (2018), “Statistical Inference for Treatment Assignment Policies,” Tech. rep., University of Wisconsin, Department of Economics.
   Google Scholar
• Rzepakowski, P., and Jaroszewicz, S. (2012), “Decision Trees for Uplift Modeling with Single and Multiple Treatments,” Knowledge and Information Systems, 32, 303–327. DOI: 10.1007/s10115-011-0434-0.
   Web of Science ®Google Scholar
• Stuard, A., and Ord, J. K. (1994), Kendall’s Advanced Theory of Statistics, Distribution Theory (Vol. 1), New York: Halsted Press.
   Google Scholar
• Tibshirani, R. (1996), “Regression Shrinkage and Selection Via the Lasso,” Journal of the Royal Statistical Society, Series B, 58, 267–288. DOI: 10.1111/j.2517-6161.1996.tb02080.x.
   Google Scholar
• van der Laan, M. J., and Rose, S. (2011), Targeted Learning: Causal Inference for Observational and Experimental Data, New York: Springer.
   Google Scholar
• Wager, S., and Athey, S. (2018), “Estimation and Inference of Heterogeneous Treatment Effects Using Random Forests,” Journal of the American Statistical Association, 113, 1228–1242. DOI: 10.1080/01621459.2017.1319839.
   Web of Science ®Google Scholar
• Zhang, B., Tsiatis, A. A., Davidian, M., and Laber, E. (2012), “Estimating Optimal Treatment Regimes From a Classification Perspective,” Statistics, 1, 103–114. DOI: 10.1002/sta.411.
   Google Scholar
• Zhao, Y., Zeng, D., Rush, J. A., and Kosorok, M. R. (2012), “Estimating Individualized Treatment Rules Using Outcome Weighted Learning,” Journal of the American Statistical Association, 107, 1106–1118. DOI: 10.1080/01621459.2012.695674.
   PubMed Web of Science ®Google Scholar
• Zhou, X., Mayer-Hamblett, N., Khan, U., and Kosorok, M. R. (2017), “Residual Weighted Learning for Estimating Individualized Treatment Rules,” Journal of the American Statistical Association, 112, 169–187. DOI: 10.1080/01621459.2015.1093947.
   PubMed Web of Science ®Google Scholar
• Zhou, Z., Athey, S., and Wager, S. (2018), “Offline Multi-Action Policy Learning: Generalization and Optimization,” Tech. rep.
   Google Scholar