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Empirical Welfare Maximization with Constraints

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  • Liyang Sun

Abstract

Empirical Welfare Maximization (EWM) is a framework that can be used to select welfare program eligibility policies based on data. This paper extends EWM by allowing for uncertainty in estimating the budget needed to implement the selected policy, in addition to its welfare. Due to the additional estimation error, I show there exist no rules that achieve the highest welfare possible while satisfying a budget constraint uniformly over a wide range of DGPs. This differs from the setting without a budget constraint where uniformity is achievable. I propose an alternative trade-off rule and illustrate it with Medicaid expansion, a setting with imperfect take-up and varying program costs.

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  • Liyang Sun, 2021. "Empirical Welfare Maximization with Constraints," Papers 2103.15298, arXiv.org, revised Sep 2024.
    • Handle: RePEc:arx:papers:2103.15298
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    References listed on IDEAS

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    11. Susan Athey & Stefan Wager, 2021. "Policy Learning With Observational Data," Econometrica, Econometric Society, vol. 89(1), pages 133-161, January.
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    Cited by:

    1. Toru Kitagawa & Hugo Lopez & Jeff Rowley, 2022. "Stochastic Treatment Choice with Empirical Welfare Updating," Papers 2211.01537, arXiv.org, revised Feb 2023.
    2. Daniel F. Pellatt, 2022. "PAC-Bayesian Treatment Allocation Under Budget Constraints," Papers 2212.09007, arXiv.org, revised Jun 2023.
    3. Kirill Ponomarev & Vira Semenova, 2024. "On the Lower Confidence Band for the Optimal Welfare," Papers 2410.07443, arXiv.org, revised Oct 2024.
    4. Toru Kitagawa & Guanyi Wang, 2023. "Individualized Treatment Allocation in Sequential Network Games," Papers 2302.05747, arXiv.org, revised Jul 2024.
    5. Yan Liu, 2022. "Policy Learning under Endogeneity Using Instrumental Variables," Papers 2206.09883, arXiv.org, revised Mar 2024.
    6. 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.
    7. Riccardo D'Adamo, 2021. "Orthogonal Policy Learning Under Ambiguity," Papers 2111.10904, arXiv.org, revised Dec 2022.
    8. 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.
    9. Qiu Hongxiang & Carone Marco & Luedtke Alex, 2022. "Individualized treatment rules under stochastic treatment cost constraints," Journal of Causal Inference, De Gruyter, vol. 10(1), pages 480-493, January.
    10. Alejandro Sanchez-Becerra, 2023. "Robust inference for the treatment effect variance in experiments using machine learning," Papers 2306.03363, arXiv.org.

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