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Model Selection for Treatment Choice: Penalized Welfare Maximization

Citations

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Cited by:

  1. Anders Bredahl Kock & David Preinerstorfer, 2024. "Regularizing Discrimination in Optimal Policy Learning with Distributional Targets," Papers 2401.17909, arXiv.org.
  2. Susan Athey & Stefan Wager, 2021. "Policy Learning With Observational Data," Econometrica, Econometric Society, vol. 89(1), pages 133-161, January.
  3. Semenova, Vira, 2023. "Debiased machine learning of set-identified linear models," Journal of Econometrics, Elsevier, vol. 235(2), pages 1725-1746.
  4. 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.
  5. Shosei Sakaguchi, 2021. "Estimation of Optimal Dynamic Treatment Assignment Rules under Policy Constraints," Papers 2106.05031, arXiv.org, revised Apr 2024.
  6. 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.
  7. Daniel F. Pellatt, 2022. "PAC-Bayesian Treatment Allocation Under Budget Constraints," Papers 2212.09007, arXiv.org, revised Jun 2023.
  8. Eric Mbakop & Max Tabord‐Meehan, 2021. "Model Selection for Treatment Choice: Penalized Welfare Maximization," Econometrica, Econometric Society, vol. 89(2), pages 825-848, March.
  9. Toru Kitagawa & Shosei Sakaguchi & Aleksey Tetenov, 2021. "Constrained Classification and Policy Learning," Papers 2106.12886, arXiv.org, revised Jul 2023.
  10. Thomas M. Russell, 2020. "Policy Transforms and Learning Optimal Policies," Papers 2012.11046, arXiv.org.
  11. Toru Kitagawa & Guanyi Wang, 2020. "Who Should Get Vaccinated? Individualized Allocation of Vaccines Over SIR Network," Papers 2012.04055, arXiv.org, revised Jul 2021.
  12. Le-Yu Chen & Sokbae Lee, 2018. "High Dimensional Classification through $\ell_0$-Penalized Empirical Risk Minimization," Papers 1811.09540, arXiv.org.
  13. 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.
  14. Max Tabord-Meehan, 2018. "Stratification Trees for Adaptive Randomization in Randomized Controlled Trials," Papers 1806.05127, arXiv.org, revised Jul 2022.
  15. Federico Crippa, 2024. "Regret Analysis in Threshold Policy Design," Papers 2404.11767, arXiv.org.
  16. Daido Kido, 2022. "Distributionally Robust Policy Learning with Wasserstein Distance," Papers 2205.04637, arXiv.org, revised Aug 2022.
  17. Christopher Adjaho & Timothy Christensen, 2022. "Externally Valid Policy Choice," Papers 2205.05561, arXiv.org, revised Jul 2023.
  18. Toru Kitagawa & Weining Wang & Mengshan Xu, 2022. "Policy Choice in Time Series by Empirical Welfare Maximization," Papers 2205.03970, arXiv.org, revised Jun 2023.
  19. Daido Kido, 2023. "Locally Asymptotically Minimax Statistical Treatment Rules Under Partial Identification," Papers 2311.08958, arXiv.org.
  20. Chunrong Ai & Yue Fang & Haitian Xie, 2024. "Data-driven Policy Learning for a Continuous Treatment," Papers 2402.02535, arXiv.org.
  21. Vira Semenova, 2023. "Adaptive Estimation of Intersection Bounds: a Classification Approach," Papers 2303.00982, arXiv.org.
  22. Toru Kitagawa & Sokbae Lee & Chen Qiu, 2022. "Treatment Choice with Nonlinear Regret," Papers 2205.08586, arXiv.org, revised Feb 2024.
  23. 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.
  24. Riccardo D'Adamo, 2021. "Orthogonal Policy Learning Under Ambiguity," Papers 2111.10904, arXiv.org, revised Dec 2022.
  25. Liyang Sun, 2021. "Empirical Welfare Maximization with Constraints," Papers 2103.15298, arXiv.org.
  26. Davide Viviano & Jelena Bradic, 2020. "Fair Policy Targeting," Papers 2005.12395, arXiv.org, revised Jun 2022.
  27. Davide Viviano & Jess Rudder, 2020. "Policy design in experiments with unknown interference," Papers 2011.08174, arXiv.org, revised May 2024.
  28. Daido Kido, 2023. "Incorporating Preferences Into Treatment Assignment Problems," Papers 2311.08963, arXiv.org.
  29. Yuya Sasaki & Takuya Ura, 2020. "Welfare Analysis via Marginal Treatment Effects," Papers 2012.07624, arXiv.org.
  30. Toru Kitagawa & Sokbae Lee & Chen Qiu, 2023. "Treatment choice, mean square regret and partial identification," The Japanese Economic Review, Springer, vol. 74(4), pages 573-602, October.
  31. Kohei Yata, 2021. "Optimal Decision Rules Under Partial Identification," Papers 2111.04926, arXiv.org, revised Aug 2023.
  32. Seungjin Han & Julius Owusu & Youngki Shin, 2022. "Statistical Treatment Rules under Social Interaction," Papers 2209.09077, arXiv.org, revised Nov 2022.
  33. Takuya Ishihara & Toru Kitagawa, 2021. "Evidence Aggregation for Treatment Choice," Papers 2108.06473, arXiv.org.
  34. 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.
  35. Alejandro Sanchez-Becerra, 2023. "Robust inference for the treatment effect variance in experiments using machine learning," Papers 2306.03363, arXiv.org.
  36. Davide Viviano, 2019. "Policy Targeting under Network Interference," Papers 1906.10258, arXiv.org, revised Apr 2024.
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