Offline Multi-Action Policy Learning: Generalization and Optimization

Citations

Cited by:

1. Shosei Sakaguchi, 2021. "Estimation of Optimal Dynamic Treatment Assignment Rules under Policy Constraints," Papers 2106.05031, arXiv.org, revised Aug 2024.
2. Martin Huber, 2019. "An introduction to flexible methods for policy evaluation," Papers 1910.00641, arXiv.org.
   • Huber, Martin, 2019. "An introduction to flexible methods for policy evaluation," FSES Working Papers 504, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
3. 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).
   • Garbero, Alessandra & Sakos, Grayson & Cerulli, Giovanni, 2021. "Towards Data-driven Project design: Providing Optimal Treatment Rules for Development Projects," 2021 Annual Meeting, August 1-3, Austin, Texas 314016, Agricultural and Applied Economics Association.
4. Susan Athey & Raj Chetty & Guido Imbens, 2020. "Using Experiments to Correct for Selection in Observational Studies," Papers 2006.09676, arXiv.org, revised May 2025.
5. Yi Zhang & Eli Ben-Michael & Kosuke Imai, 2022. "Safe Policy Learning under Regression Discontinuity Designs with Multiple Cutoffs," Papers 2208.13323, arXiv.org, revised Sep 2024.
6. Matthew A. Masten, 2023. "Minimax-regret treatment rules with many treatments," The Japanese Economic Review, Springer, vol. 74(4), pages 501-537, October.
7. Robert Donnelly & Francisco J. R. Ruiz & David Blei & Susan Athey, 2021. "Correction to: Counterfactual inference for consumer choice across many product categories," Quantitative Marketing and Economics (QME), Springer, vol. 19(3), pages 409-409, December.
   • Robert Donnelly & Francisco J.R. Ruiz & David Blei & Susan Athey, 2021. "Counterfactual inference for consumer choice across many product categories," Quantitative Marketing and Economics (QME), Springer, vol. 19(3), pages 369-407, December.
   • Rob Donnelly & Francisco R. Ruiz & David Blei & Susan Athey, 2019. "Counterfactual Inference for Consumer Choice Across Many Product Categories," Papers 1906.02635, arXiv.org, revised Aug 2023.
8. Athey, Susan & Imbens, Guido W., 2019. "Machine Learning Methods Economists Should Know About," Research Papers 3776, Stanford University, Graduate School of Business.
   • Susan Athey & Guido Imbens, 2019. "Machine Learning Methods Economists Should Know About," Papers 1903.10075, arXiv.org.
9. Cockx, Bart & Lechner, Michael & Bollens, Joost, 2023. "Priority to unemployed immigrants? A causal machine learning evaluation of training in Belgium," Labour Economics, Elsevier, vol. 80(C).
   • Bart Cockx & Michael Lechner & Joost Bollens, 2019. "Priority to unemployed immigrants? A causal machine learning evaluation of training in Belgium," Papers 1912.12864, arXiv.org, revised Dec 2022.
   • Bart Cockx & Michael Lechner & Joost Bollens, 2020. "Priority of Unemployed Immigrants? A Causal Machine Learning Evaluation of Training in Belgium," CESifo Working Paper Series 8297, CESifo.
   • Lechner, Michael & Cockx, Bart & Bollens, Joost, 2020. "Priority to unemployed immigrants? A causal machine learning evaluation of training in Belgium," CEPR Discussion Papers 14270, C.E.P.R. Discussion Papers.
   • Cockx, Bart & Lechner, Michael & Bollens, Joost, 2020. "Priority to unemployed immigrants? A causal machine learning evaluation of training in Belgium," ROA Research Memorandum 006, Maastricht University, Research Centre for Education and the Labour Market (ROA).
   • Cockx, Bart & Lechner, Michael & Bollens, Joost, 2020. "Priority to unemployed immigrants? A causal machine learning evaluation of training in Belgium," Economics Working Paper Series 2001, University of St. Gallen, School of Economics and Political Science.
   • Cockx, Bart & Lechner, Michael & Bollens, Joost, 2020. "Priority to unemployed immigrants? A causal machine learning evaluation of training in Belgium," Research Memorandum 015, Maastricht University, Graduate School of Business and Economics (GSBE).
   • Cockx, Bart & Lechner, Michael & Bollens, Joost, 2019. "Priority to Unemployed Immigrants? A Causal Machine Learning Evaluation of Training in Belgium," IZA Discussion Papers 12875, Institute of Labor Economics (IZA).
   • Bart Cockx & Michael Lechner & Joost Bollens, 2020. "Priority to unemployed immigrants? A causal machine learning evaluation of training in Belgium," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 20/998, Ghent University, Faculty of Economics and Business Administration.
   • Bart Cockx & Michael Lechner & Joost Bollens, 2020. "Priority to unemployed immigrants? A causal machine learning evaluation of training in Belgium," LIDAM Discussion Papers IRES 2020016, Université catholique de Louvain, Institut de Recherches Economiques et Sociales (IRES).
10. 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.
    • Zhan, Ruohan & Ren, Zhimei & Athey, Susan & Zhou, Zhengyuan, 2021. "Policy Learning with Adaptively Collected Data," Research Papers 3963, Stanford University, Graduate School of Business.
    • Ruohan Zhan & Zhimei Ren & Susan Athey & Zhengyuan Zhou, 2021. "Policy Learning with Adaptively Collected Data," Papers 2105.02344, arXiv.org, revised Nov 2022.
11. Zequn Jin & Gaoqian Xu & Xi Zheng & Yahong Zhou, 2025. "Policy Learning under Unobserved Confounding: A Robust and Efficient Approach," Papers 2507.20550, arXiv.org.
12. Patrick Rehill & Nicholas Biddle, 2025. "Policy Learning for Many Outcomes of Interest: Combining Optimal Policy Trees with Multi-objective Bayesian Optimisation," Computational Economics, Springer;Society for Computational Economics, vol. 66(2), pages 971-1001, August.
13. Zhaonan Qu & Isabella Qian & Zhengyuan Zhou, 2020. "Interpretable Personalization via Policy Learning with Linear Decision Boundaries," Papers 2003.07545, arXiv.org, revised Nov 2022.
14. 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.
    • Knaus, Michael C., 2020. "Double Machine Learning Based Program Evaluation under Unconfoundedness," IZA Discussion Papers 13051, Institute of Labor Economics (IZA).
    • Knaus, Michael C., 2020. "Double Machine Learning based Program Evaluation under Unconfoundedness," Economics Working Paper Series 2004, University of St. Gallen, School of Economics and Political Science.
    • Michael C. Knaus, 2020. "Double Machine Learning based Program Evaluation under Unconfoundedness," Papers 2003.03191, arXiv.org, revised Jun 2022.
15. Danielle Li & Lindsey R. Raymond & Peter Bergman, 2020. "Hiring as Exploration," NBER Working Papers 27736, National Bureau of Economic Research, Inc.
    • Danielle Li & Lindsey Raymond & Peter Bergman, 2024. "Hiring as Exploration," Papers 2411.03616, arXiv.org.
16. Davide Viviano, 2019. "Policy Targeting under Network Interference," Papers 1906.10258, arXiv.org, revised Apr 2024.
17. Goller, Daniel & Lechner, Michael & Pongratz, Tamara & Wolff, Joachim, 2025. "Active labor market policies for the long-term unemployed: New evidence from causal machine learning," Labour Economics, Elsevier, vol. 94(C).
    • Daniel Goller & Tamara Harrer & Michael Lechner & Joachim Wolff, 2021. "Active labour market policies for the long-term unemployed: New evidence from causal machine learning," Papers 2106.10141, arXiv.org, revised May 2023.
    • Goller, Daniel & Harrer, Tamara & Lechner, Michael & Wolff, Joachim, 2021. "Active labour market policies for the long-term unemployed: New evidence from causal machine learning," Economics Working Paper Series 2108, University of St. Gallen, School of Economics and Political Science.
    • Goller, Daniel & Harrer, Tamara & Lechner, Michael & Wolff, Joachim, 2021. "Active Labour Market Policies for the Long-Term Unemployed: New Evidence from Causal Machine Learning," IZA Discussion Papers 14486, Institute of Labor Economics (IZA).
18. 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.
19. James Cussens & Julia Hatamyar & Vishalie Shah & Noemi Kreif, 2025. "Fast Learning of Optimal Policy Trees," Papers 2506.15435, arXiv.org.
20. 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.
    • Tobias Cagala & Ulrich Glogowsky & Johannes Rincke & Anthony Strittmatter, 2021. "Optimal Targeting in Fundraising: A Machine-Learning Approach," CESifo Working Paper Series 9037, CESifo.
21. Masahiro Kato, 2020. "Confidence Interval for Off-Policy Evaluation from Dependent Samples via Bandit Algorithm: Approach from Standardized Martingales," Papers 2006.06982, arXiv.org.
22. Anya Shchetkina, 2025. "Blind Targeting: Personalization under Third-Party Privacy Constraints," Papers 2507.05175, arXiv.org.
23. Toru Kitagawa & Guanyi Wang, 2020. "Who Should Get Vaccinated? Individualized Allocation of Vaccines Over SIR Network," Papers 2012.04055, arXiv.org, revised Jul 2021.
24. Albert Chiu, 2025. "An Algorithm for Identifying Interpretable Subgroups With Elevated Treatment Effects," Papers 2507.09494, arXiv.org.
25. 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.
26. Athey, Susan & Keleher, Niall & Spiess, Jann, 2025. "Machine learning who to nudge: Causal vs predictive targeting in a field experiment on student financial aid renewal," Journal of Econometrics, Elsevier, vol. 249(PC).
    • Susan Athey & Niall Keleher & Jann Spiess, 2023. "Machine Learning Who to Nudge: Causal vs Predictive Targeting in a Field Experiment on Student Financial Aid Renewal," Papers 2310.08672, arXiv.org, revised May 2024.
    • Athey, Susan & Keleher, Niall & Spiess, Jann, 2023. "Machine Learning Who to Nudge: Causal vs Predictive Targeting in a Field Experiment on Student Financial Aid Renewal," Research Papers 4146, Stanford University, Graduate School of Business.
27. Yanqin Fan & Yuan Qi & Gaoqian Xu, 2025. "Policy Learning with $\alpha$-Expected Welfare," Papers 2505.00256, arXiv.org.
28. 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.
29. 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.
30. Jann Spiess & Vasilis Syrgkanis & Victor Yaneng Wang, 2021. "Finding Subgroups with Significant Treatment Effects," Papers 2103.07066, arXiv.org, revised Dec 2023.