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Who should be treated? Empirical welfare maximization methods for treatment choice

Citations

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Cited by:

  1. Kyle Colangelo & Ying-Ying Lee, 2020. "Double Debiased Machine Learning Nonparametric Inference with Continuous Treatments," Papers 2004.03036, arXiv.org, revised Sep 2023.
  2. Yoichi Arai & Yu‐Chin Hsu & Toru Kitagawa & Ismael Mourifié & Yuanyuan Wan, 2022. "Testing identifying assumptions in fuzzy regression discontinuity designs," Quantitative Economics, Econometric Society, vol. 13(1), pages 1-28, January.
  3. Kyle Colangelo & Ying-Ying Lee, 2019. "Double debiased machine learning nonparametric inference with continuous treatments," CeMMAP working papers CWP72/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  4. Manski, Charles F., 2023. "Probabilistic prediction for binary treatment choice: With focus on personalized medicine," Journal of Econometrics, Elsevier, vol. 234(2), pages 647-663.
  5. Lihua Lei & Roshni Sahoo & Stefan Wager, 2023. "Policy Learning under Biased Sample Selection," Papers 2304.11735, arXiv.org.
  6. Cordier, J.; & Salvi, I.; & Steinbeck, V.; & Geissler, A.; & Vogel, J.;, 2023. "Is rapid recovery always the best recovery? - Developing a machine learning approach for optimal assignment rules under capacity constraints for knee replacement patients," Health, Econometrics and Data Group (HEDG) Working Papers 23/08, HEDG, c/o Department of Economics, University of York.
  7. Toru Kitagawa & Aleksey Tetenov, 2021. "Equality-Minded Treatment Choice," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(2), pages 561-574, March.
  8. Martin Huber, 2019. "An introduction to flexible methods for policy evaluation," Papers 1910.00641, arXiv.org.
  9. Maximilian Kasy & Anja Sautmann, 2021. "Adaptive Treatment Assignment in Experiments for Policy Choice," Econometrica, Econometric Society, vol. 89(1), pages 113-132, January.
  10. Yi Zhang & Kosuke Imai, 2023. "Individualized Policy Evaluation and Learning under Clustered Network Interference," Papers 2311.02467, arXiv.org, revised Feb 2024.
  11. Battistin,Erich & De Nadai,Michele & Krishnan,Nandini, 2020. "The Insights and Illusions of Consumption Measurements," Policy Research Working Paper Series 9255, The World Bank.
  12. Athey, Susan & Imbens, Guido W., 2019. "Machine Learning Methods Economists Should Know About," Research Papers 3776, Stanford University, Graduate School of Business.
  13. Le‐Yu Chen & Sokbae Lee, 2018. "Exact computation of GMM estimators for instrumental variable quantile regression models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(4), pages 553-567, June.
  14. Hoshino Tadao & Yanagi Takahide, 2022. "Estimating marginal treatment effects under unobserved group heterogeneity," Journal of Causal Inference, De Gruyter, vol. 10(1), pages 197-216, January.
  15. Chen, Le-Yu & Lee, Sokbae, 2018. "Best subset binary prediction," Journal of Econometrics, Elsevier, vol. 206(1), pages 39-56.
  16. Vira Semenova, 2023. "Aggregated Intersection Bounds and Aggregated Minimax Values," Papers 2303.00982, arXiv.org, revised Jun 2024.
  17. Aldo Gael Carranza & Susan Athey, 2023. "Federated Offline Policy Learning," Papers 2305.12407, arXiv.org, revised Oct 2024.
  18. Steven F. Lehrer & R. Vincent Pohl & Kyungchul Song, 2022. "Multiple Testing and the Distributional Effects of Accountability Incentives in Education," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(4), pages 1552-1568, October.
  19. Battistin, Erich & De Nadai, Michele & Krishnan, Nandini, 2023. "The insights and illusions of consumption measurements," Journal of Development Economics, Elsevier, vol. 161(C).
  20. Alejandro Sanchez-Becerra, 2023. "Robust inference for the treatment effect variance in experiments using machine learning," Papers 2306.03363, arXiv.org.
  21. Davide Viviano, 2019. "Policy Targeting under Network Interference," Papers 1906.10258, arXiv.org, revised Apr 2024.
  22. Nygaard, Vegard M. & Sørensen, Bent E. & Wang, Fan, 2022. "Optimal allocations to heterogeneous agents with an application to stimulus checks," Journal of Economic Dynamics and Control, Elsevier, vol. 138(C).
  23. Isaiah Andrews & Toru Kitagawa & Adam McCloskey, 2024. "Inference on Winners," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 139(1), pages 305-358.
  24. Juliano Assunção & Robert McMillan & Joshua Murphy & Eduardo Souza-Rodrigues, 2019. "Optimal Environmental Targeting in the Amazon Rainforest," NBER Working Papers 25636, National Bureau of Economic Research, Inc.
  25. Susan Athey & Stefan Wager, 2021. "Policy Learning With Observational Data," Econometrica, Econometric Society, vol. 89(1), pages 133-161, January.
  26. Max Tabord-Meehan, 2023. "Stratification Trees for Adaptive Randomisation in Randomised Controlled Trials," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 90(5), pages 2646-2673.
  27. Weibin Mo & Yufeng Liu, 2022. "Efficient learning of optimal individualized treatment rules for heteroscedastic or misspecified treatment‐free effect models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(2), pages 440-472, April.
  28. Daniel F. Pellatt, 2022. "PAC-Bayesian Treatment Allocation Under Budget Constraints," Papers 2212.09007, arXiv.org, revised Jun 2023.
  29. Santiago Acerenza & Vitor Possebom & Pedro H. C. Sant'Anna, 2023. "Was Javert right to be suspicious? Unpacking treatment effect heterogeneity of alternative sentences on time-to-recidivism in Brazil," Papers 2311.13969, arXiv.org, revised May 2024.
  30. Mert Demirer & Vasilis Syrgkanis & Greg Lewis & Victor Chernozhukov, 2019. "Semi-Parametric Efficient Policy Learning with Continuous Actions," CeMMAP working papers CWP34/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  31. Koichiro Ito & Takanori Ida & Makoto Tanaka, 2023. "Selection on Welfare Gains: Experimental Evidence from Electricity Plan Choice," American Economic Review, American Economic Association, vol. 113(11), pages 2937-2973, November.
  32. Gyungbae Park, 2024. "Debiased Machine Learning when Nuisance Parameters Appear in Indicator Functions," Papers 2403.15934, arXiv.org.
  33. Charles F. Manski, 2019. "Statistical inference for statistical decisions," Papers 1909.06853, arXiv.org.
  34. Roshni Sahoo & Stefan Wager, 2022. "Policy Learning with Competing Agents," Papers 2204.01884, arXiv.org, revised Apr 2024.
  35. Michael C Knaus, 2022. "Double machine learning-based programme evaluation under unconfoundedness [Econometric methods for program evaluation]," The Econometrics Journal, Royal Economic Society, vol. 25(3), pages 602-627.
  36. Ruohan Zhan & Zhimei Ren & Susan Athey & Zhengyuan Zhou, 2024. "Policy Learning with Adaptively Collected Data," Management Science, INFORMS, vol. 70(8), pages 5270-5297, August.
  37. Daido Kido, 2023. "Locally Asymptotically Minimax Statistical Treatment Rules Under Partial Identification," Papers 2311.08958, arXiv.org.
  38. Achim Ahrens & Alessandra Stampi-Bombelli & Selina Kurer & Dominik Hangartner, 2023. "Optimal multi-action treatment allocation: A two-phase field experiment to boost immigrant naturalization," Papers 2305.00545, arXiv.org, revised Feb 2024.
  39. Takanori Ida & Takunori Ishihara & Koichiro Ito & Daido Kido & Toru Kitagawa & Shosei Sakaguchi & Shusaku Sasaki, 2022. "Choosing Who Chooses: Selection-Driven Targeting in Energy Rebate Programs," NBER Working Papers 30469, National Bureau of Economic Research, Inc.
  40. Anders Bredahl Kock & David Preinerstorfer & Bezirgen Veliyev, 2022. "Functional Sequential Treatment Allocation," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 117(539), pages 1311-1323, September.
  41. Eric Mbakop & Max Tabord‐Meehan, 2021. "Model Selection for Treatment Choice: Penalized Welfare Maximization," Econometrica, Econometric Society, vol. 89(2), pages 825-848, March.
  42. Yuya Sasaki & Takuya Ura, 2020. "Welfare Analysis via Marginal Treatment Effects," Papers 2012.07624, arXiv.org.
  43. Takuya Ishihara & Toru Kitagawa, 2021. "Evidence Aggregation for Treatment Choice," Papers 2108.06473, arXiv.org, revised Jul 2024.
  44. Patrick Kline & Christopher Walters, 2019. "Audits as Evidence: Experiments, Ensembles, and Enforcement," Papers 1907.06622, arXiv.org, revised Jul 2019.
  45. Kyle Colangelo & Ying-Ying Lee, 2019. "Double debiased machine learning nonparametric inference with continuous treatments," CeMMAP working papers CWP54/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  46. Shosei Sakaguchi, 2021. "Estimation of Optimal Dynamic Treatment Assignment Rules under Policy Constraints," Papers 2106.05031, arXiv.org, revised Aug 2024.
  47. Christensen, Peter & Francisco, Paul & Myers, Erica & Shao, Hansen & Souza, Mateus, 2024. "Energy efficiency can deliver for climate policy: Evidence from machine learning-based targeting," Journal of Public Economics, Elsevier, vol. 234(C).
  48. Toru Kitagawa & Shosei Sakaguchi & Aleksey Tetenov, 2021. "Constrained Classification and Policy Learning," Papers 2106.12886, arXiv.org, revised Jul 2023.
  49. Zhengyuan Zhou & Susan Athey & Stefan Wager, 2023. "Offline Multi-Action Policy Learning: Generalization and Optimization," Operations Research, INFORMS, vol. 71(1), pages 148-183, January.
  50. Bertrand,Marianne & Crepon,Bruno Jacques Jean Philippe & Marguerie,Alicia Charlene & Premand,Patrick, 2021. "Do Workfare Programs Live Up to Their Promises ? Experimental Evidence from Côte d’Ivoire," Policy Research Working Paper Series 9611, The World Bank.
  51. Anders Bredahl Kock & Martin Thyrsgaard, 2017. "Optimal sequential treatment allocation," Papers 1705.09952, arXiv.org, revised Aug 2018.
  52. Garbero, Alessandra & Sakos, Grayson & Cerulli, Giovanni, 2023. "Towards data-driven project design: Providing optimal treatment rules for development projects," Socio-Economic Planning Sciences, Elsevier, vol. 89(C).
  53. Augustine Denteh & Helge Liebert, 2022. "Who Increases Emergency Department Use? New Insights from the Oregon Health Insurance Experiment," CESifo Working Paper Series 9664, CESifo.
  54. Saskia Opitz & Dirk Sliwka & Timo Vogelsang & Tom Zimmermann, 2022. "The Targeted Assignment of Incentive Schemes," ECONtribute Discussion Papers Series 187, University of Bonn and University of Cologne, Germany.
  55. Henrika Langen & Martin Huber, 2022. "How causal machine learning can leverage marketing strategies: Assessing and improving the performance of a coupon campaign," Papers 2204.10820, arXiv.org, revised Jun 2022.
  56. Chunrong Ai & Yue Fang & Haitian Xie, 2024. "Data-driven Policy Learning for Continuous Treatments," Papers 2402.02535, arXiv.org, revised Nov 2024.
  57. Toru Kitagawa & Aleksey Tetenov, 2017. "Equality-minded treatment choice," CeMMAP working papers 10/17, Institute for Fiscal Studies.
  58. Susan Athey, 2018. "The Impact of Machine Learning on Economics," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 507-547, National Bureau of Economic Research, Inc.
  59. Daido Kido, 2023. "Incorporating Preferences Into Treatment Assignment Problems," Papers 2311.08963, arXiv.org.
  60. Youngki Shin & Zvezdomir Todorov, 2021. "Exact computation of maximum rank correlation estimator," The Econometrics Journal, Royal Economic Society, vol. 24(3), pages 589-607.
  61. Firpo, Sergio & Galvao, Antonio F. & Kobus, Martyna & Parker, Thomas & Rosa-Dias, Pedro, 2020. "Loss Aversion and the Welfare Ranking of Policy Interventions," IZA Discussion Papers 13176, Institute of Labor Economics (IZA).
  62. Kohei Yata, 2021. "Optimal Decision Rules Under Partial Identification," Papers 2111.04926, arXiv.org, revised Aug 2023.
  63. Samuel Higbee, 2022. "Policy Learning with New Treatments," Papers 2210.04703, arXiv.org, revised Sep 2023.
  64. Jeff Dominitz & Charles F. Manski, 2024. "Comprehensive OOS Evaluation of Predictive Algorithms with Statistical Decision Theory," Papers 2403.11016, arXiv.org, revised May 2024.
  65. Andrew Bennett & Nathan Kallus, 2020. "Efficient Policy Learning from Surrogate-Loss Classification Reductions," Papers 2002.05153, arXiv.org.
  66. Karun Adusumilli & Friedrich Geiecke & Claudio Schilter, 2019. "Dynamically Optimal Treatment Allocation," Papers 1904.01047, arXiv.org, revised Nov 2024.
  67. Abhijit Banerjee & Sylvain Chassang & Sergio Montero & Erik Snowberg, 2017. "A Theory of Experimenters," CESifo Working Paper Series 6678, CESifo.
  68. Charles F. Manski, 2020. "Towards Reasonable Patient Care Under Uncertainty," Contemporary Economic Policy, Western Economic Association International, vol. 38(2), pages 227-245, April.
  69. Undral Byambadalai, 2022. "Identification and Inference for Welfare Gains without Unconfoundedness," Papers 2207.04314, arXiv.org.
  70. Kenshi Abe & Yusuke Kaneko, 2020. "Off-Policy Exploitability-Evaluation in Two-Player Zero-Sum Markov Games," Papers 2007.02141, arXiv.org, revised Dec 2020.
  71. Masahiro Kato & Masaaki Imaizumi & Takuya Ishihara & Toru Kitagawa, 2022. "Best Arm Identification with Contextual Information under a Small Gap," Papers 2209.07330, arXiv.org, revised Jan 2023.
  72. Tobias Cagala & Ulrich Glogowsky & Johannes Rincke & Anthony Strittmatter, 2021. "Optimal Targeting in Fundraising: A Machine-Learning Approach," Economics working papers 2021-08, Department of Economics, Johannes Kepler University Linz, Austria.
  73. Toru Kitagawa & Guanyi Wang, 2020. "Who Should Get Vaccinated? Individualized Allocation of Vaccines Over SIR Network," Papers 2012.04055, arXiv.org, revised Jul 2021.
  74. Charles F. Manski & Aleksey Tetenov, 2023. "Statistical decision theory respecting stochastic dominance," The Japanese Economic Review, Springer, vol. 74(4), pages 447-469, October.
  75. Keisuke Hirano & Jack R. Porter, 2016. "Panel Asymptotics and Statistical Decision Theory," The Japanese Economic Review, Japanese Economic Association, vol. 67(1), pages 33-49, March.
  76. Takanori Ida & Takunori Ishihara & Koichiro Ito & Daido Kido & Toru Kitagawa & Shosei Sakaguchi & Shusaku Sasaki, 2021. "Paternalism, Autonomy, or Both? Experimental Evidence from Energy Saving Programs," Papers 2112.09850, arXiv.org.
  77. Julia Hatamyar & Noemi Kreif, 2023. "Policy Learning with Rare Outcomes," Papers 2302.05260, arXiv.org, revised Oct 2023.
  78. Hema Yoganarasimhan & Ebrahim Barzegary & Abhishek Pani, 2020. "Design and Evaluation of Personalized Free Trials," Papers 2006.13420, arXiv.org.
  79. Toru Kitagawa & Sokbae Lee & Chen Qiu, 2022. "Treatment Choice with Nonlinear Regret," Papers 2205.08586, arXiv.org, revised Oct 2024.
  80. Toru Kitagawa & Guanyi Wang, 2020. "Who should get vaccinated? Individualized allocation of vaccines over SIR network," CeMMAP working papers CWP59/20, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  81. Toru Kitagawa & Jeff Rowley, 2024. "Bandit algorithms for policy learning: methods, implementation, and welfare-performance," The Japanese Economic Review, Springer, vol. 75(3), pages 407-447, July.
  82. Davide Viviano & Jelena Bradic, 2020. "Fair Policy Targeting," Papers 2005.12395, arXiv.org, revised Jun 2022.
  83. Ganesh Karapakula, 2023. "Stable Probability Weighting: Large-Sample and Finite-Sample Estimation and Inference Methods for Heterogeneous Causal Effects of Multivalued Treatments Under Limited Overlap," Papers 2301.05703, arXiv.org, revised Jan 2023.
  84. Isaiah Andrews & Jesse M. Shapiro, 2021. "A Model of Scientific Communication," Econometrica, Econometric Society, vol. 89(5), pages 2117-2142, September.
  85. Maximilian Kasy, 2023. "The political economy of AI: Towards democratic control of the means of prediction," Economics Series Working Papers 1014, University of Oxford, Department of Economics.
  86. Yusuke Narita, 2018. "Experiment-as-Market: Incorporating Welfare into Randomized Controlled Trials," Cowles Foundation Discussion Papers 2127r, Cowles Foundation for Research in Economics, Yale University, revised May 2019.
  87. Juan Carlos Perdomo, 2023. "The Relative Value of Prediction in Algorithmic Decision Making," Papers 2312.08511, arXiv.org, revised May 2024.
  88. A Stefano Caria & Grant Gordon & Maximilian Kasy & Simon Quinn & Soha Osman Shami & Alexander Teytelboym, 2024. "An Adaptive Targeted Field Experiment: Job Search Assistance for Refugees in Jordan," Journal of the European Economic Association, European Economic Association, vol. 22(2), pages 781-836.
  89. Jiawei Fu & Tara Slough, 2024. "Heterogeneous Treatment Effects and Causal Mechanisms," Papers 2404.01566, arXiv.org, revised Jun 2024.
  90. Giovanni Cerulli, 2020. "Optimal Policy Learning: From Theory to Practice," Papers 2011.04993, arXiv.org.
  91. Federico Crippa, 2024. "Regret Analysis in Threshold Policy Design," Papers 2404.11767, arXiv.org.
  92. Ashesh Rambachan & Amanda Coston & Edward Kennedy, 2022. "Robust Design and Evaluation of Predictive Algorithms under Unobserved Confounding," Papers 2212.09844, arXiv.org, revised May 2024.
  93. Davide Viviano & Jess Rudder, 2020. "Policy design in experiments with unknown interference," Papers 2011.08174, arXiv.org, revised May 2024.
  94. Yuchen Hu & Henry Zhu & Emma Brunskill & Stefan Wager, 2024. "Minimax-Regret Sample Selection in Randomized Experiments," Papers 2403.01386, arXiv.org, revised Jun 2024.
  95. Yuehao Bai & Azeem M. Shaikh & Max Tabord-Meehan, 2024. "A Primer on the Analysis of Randomized Experiments and a Survey of some Recent Advances," Papers 2405.03910, arXiv.org.
  96. Walter W. Zhang & Sanjog Misra, 2022. "Coarse Personalization," Papers 2204.05793, arXiv.org, revised Aug 2024.
  97. Harrison H. Li & Art B. Owen, 2023. "Double machine learning and design in batch adaptive experiments," Papers 2309.15297, arXiv.org.
  98. Charles F. Manski, 2021. "Econometrics for Decision Making: Building Foundations Sketched by Haavelmo and Wald," Econometrica, Econometric Society, vol. 89(6), pages 2827-2853, November.
  99. Anders Bredahl Kock & David Preinerstorfer, 2024. "Regularizing Discrimination in Optimal Policy Learning with Distributional Targets," Papers 2401.17909, arXiv.org.
  100. Zhengyu Zhang & Zequn Jin & Lihua Lin, 2024. "Identification and inference of outcome conditioned partial effects of general interventions," Papers 2407.16950, arXiv.org.
  101. Maximilian Blesch & Philipp Eisenhauer, 2023. "Robust Decision-Making under Risk and Ambiguity," Rationality and Competition Discussion Paper Series 463, CRC TRR 190 Rationality and Competition.
  102. Xiaohong Chen & Zhengling Qi & Runzhe Wan, 2023. "STEEL: Singularity-aware Reinforcement Learning," Papers 2301.13152, arXiv.org, revised Jun 2024.
  103. Hugo Bodory & Federica Mascolo & Michael Lechner, 2024. "Enabling Decision-Making with the Modified Causal Forest: Policy Trees for Treatment Assignment," Papers 2406.02241, arXiv.org.
  104. Shosei Sakaguchi, 2024. "Policy Learning for Optimal Dynamic Treatment Regimes with Observational Data," Papers 2404.00221, arXiv.org, revised Dec 2024.
  105. Nathan Kallus, 2022. "Treatment Effect Risk: Bounds and Inference," Papers 2201.05893, arXiv.org, revised Jul 2022.
  106. Kitagawa, Toru & Wang, Guanyi, 2023. "Who should get vaccinated? Individualized allocation of vaccines over SIR network," Journal of Econometrics, Elsevier, vol. 232(1), pages 109-131.
  107. Charles F. Manski, 2019. "Meta-Analysis for Medical Decisions," NBER Working Papers 25504, National Bureau of Economic Research, Inc.
  108. Daido Kido, 2022. "Distributionally Robust Policy Learning with Wasserstein Distance," Papers 2205.04637, arXiv.org, revised Aug 2022.
  109. Toru Kitagawa & Weining Wang & Mengshan Xu, 2022. "Policy Choice in Time Series by Empirical Welfare Maximization," Papers 2205.03970, arXiv.org, revised Dec 2024.
  110. Timothy Christensen & Hyungsik Roger Moon & Frank Schorfheide, 2020. "Robust Forecasting," Papers 2011.03153, arXiv.org, revised Dec 2020.
  111. Tobias Cagala & Ulrich Glogowsky & Johannes Rincke & Anthony Strittmatter, 2021. "Optimal Targeting in Fundraising: A Causal Machine-Learning Approach," Papers 2103.10251, arXiv.org, revised Sep 2021.
  112. Battiston, Pietro & Gamba, Simona & Santoro, Alessandro, 2024. "Machine learning and the optimization of prediction-based policies," Technological Forecasting and Social Change, Elsevier, vol. 199(C).
  113. Bilias, Yannis & Florios, Kostas & Skouras, Spyros, 2019. "Exact computation of Censored Least Absolute Deviations estimator," Journal of Econometrics, Elsevier, vol. 212(2), pages 584-606.
  114. Guido W. Imbens, 2020. "Potential Outcome and Directed Acyclic Graph Approaches to Causality: Relevance for Empirical Practice in Economics," Journal of Economic Literature, American Economic Association, vol. 58(4), pages 1129-1179, December.
  115. Bjorkegren, Dan & Blumenstock, Joshua & Knight, Samsun, 2022. "(Machine) Learning What Policies Value," CEPR Discussion Papers 17364, C.E.P.R. Discussion Papers.
  116. Li,Shanjun & Xing,Jianwei & Yang,Lin & Zhang,Fan, 2020. "Transportation and the Environment : A Review of Empirical Literature," Policy Research Working Paper Series 9421, The World Bank.
  117. Toru Kitagawa & Jeff Rowley, 2022. "von Mises-Fisher distributions and their statistical divergence," Papers 2202.05192, arXiv.org, revised Nov 2022.
  118. Toru Kitagawa & Hugo Lopez & Jeff Rowley, 2022. "Stochastic Treatment Choice with Empirical Welfare Updating," Papers 2211.01537, arXiv.org, revised Feb 2023.
  119. Ryo Okui, 2024. "The 2023 Japanese Economic Association Nakahara Prize: Recipient—Prof. Toru Kitagawa, Brown University and University College London," The Japanese Economic Review, Springer, vol. 75(3), pages 405-406, July.
  120. Semenova, Vira, 2023. "Debiased machine learning of set-identified linear models," Journal of Econometrics, Elsevier, vol. 235(2), pages 1725-1746.
  121. Toru Kitagawa & Guanyi Wang, 2021. "Who should get vaccinated? Individualized allocation of vaccines over SIR network," CeMMAP working papers CWP28/21, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  122. Dillon Bowen, 2022. "Simple models predict behavior at least as well as behavioral scientists," Papers 2208.01167, arXiv.org.
  123. Xiaoxue Sherry Gao & Glenn W. Harrison & Rusty Tchernis, 2023. "Behavioral welfare economics and risk preferences: a Bayesian approach," Experimental Economics, Springer;Economic Science Association, vol. 26(2), pages 273-303, April.
  124. Le-Yu Chen & Sokbae Lee, 2018. "High Dimensional Classification through $\ell_0$-Penalized Empirical Risk Minimization," Papers 1811.09540, arXiv.org.
  125. Kock, Anders Bredahl & Preinerstorfer, David & Veliyev, Bezirgen, 2023. "Treatment recommendation with distributional targets," Journal of Econometrics, Elsevier, vol. 234(2), pages 624-646.
  126. Christopher Adjaho & Timothy Christensen, 2022. "Externally Valid Policy Choice," Papers 2205.05561, arXiv.org, revised Jul 2023.
  127. Yu-Chang Chen & Haitian Xie, 2022. "Personalized Subsidy Rules," Papers 2202.13545, arXiv.org, revised Mar 2022.
  128. Juliano Assuncao & Robert McMillan & Joshua Murphy & Eduardo Souza-Rodrigues, 2019. "Optimal Environmental Targeting in the Amazon Rainforest," Working Papers tecipa-631, University of Toronto, Department of Economics.
  129. Seungjin Han & Julius Owusu & Youngki Shin, 2022. "Statistical Treatment Rules under Social Interaction," Papers 2209.09077, arXiv.org, revised Nov 2022.
  130. Haitian Xie, 2020. "Finite-Sample Average Bid Auction," Papers 2008.10217, arXiv.org, revised Feb 2022.
  131. Gabriel Okasa, 2022. "Meta-Learners for Estimation of Causal Effects: Finite Sample Cross-Fit Performance," Papers 2201.12692, arXiv.org.
  132. Haushofer, Johannes & Niehaus, Paul & Paramo, Carlos & Miguel, Edward & Walker, Michael W, 2022. "Targeting Impact Versus Deprivation," Department of Economics, Working Paper Series qt07j8n9vz, Department of Economics, Institute for Business and Economic Research, UC Berkeley.
  133. Thomas M. Russell, 2020. "Policy Transforms and Learning Optimal Policies," Papers 2012.11046, arXiv.org.
  134. Jaime Ramirez-Cuellar, 2023. "Testing for idiosyncratic Treatment Effect Heterogeneity," Papers 2304.01141, arXiv.org.
  135. Liyang Sun, 2021. "Empirical Welfare Maximization with Constraints," Papers 2103.15298, arXiv.org, revised Sep 2024.
  136. Toru Kitagawa & Guanyi Wang, 2023. "Individualized Treatment Allocation in Sequential Network Games," Papers 2302.05747, arXiv.org, revised Jul 2024.
  137. Susan Athey & Undral Byambadalai & Vitor Hadad & Sanath Kumar Krishnamurthy & Weiwen Leung & Joseph Jay Williams, 2022. "Contextual Bandits in a Survey Experiment on Charitable Giving: Within-Experiment Outcomes versus Policy Learning," Papers 2211.12004, arXiv.org.
  138. Anders Bredahl Kock & David Preinerstorfer & Bezirgen Veliyev, 2020. "Functional Sequential Treatment Allocation with Covariates," Papers 2001.10996, arXiv.org.
  139. Davide Viviano & Kaspar Wuthrich & Paul Niehaus, 2021. "A model of multiple hypothesis testing," Papers 2104.13367, arXiv.org, revised Apr 2024.
  140. Kai Feng & Han Hong & Ke Tang & Jingyuan Wang, 2023. "Statistical Tests for Replacing Human Decision Makers with Algorithms," Papers 2306.11689, arXiv.org, revised Dec 2024.
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