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Empirical Welfare Maximization with Constraints

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  • Liyang Sun

Abstract

When designing eligibility criteria for welfare programs, policymakers naturally want to target the individuals who will benefit the most. This paper proposes two new econometric approaches to selecting an optimal eligibility criterion when individuals' costs to the program are unknown and need to be estimated. One is designed to achieve the highest benefit possible while satisfying a budget constraint with high probability. The other is designed to optimally trade off the benefit and the cost from violating the budget constraint. The setting I consider extends the previous literature on Empirical Welfare Maximization by allowing for uncertainty in estimating the budget needed to implement the criterion, in addition to its benefit. Consequently, my approaches improve the existing approach as they can be applied to settings with imperfect take-up or varying program needs. I illustrate my approaches empirically by deriving an optimal budget-constrained Medicaid expansion in the US.

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

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    5. Eric Mbakop & Max Tabord‐Meehan, 2021. "Model Selection for Treatment Choice: Penalized Welfare Maximization," Econometrica, Econometric Society, vol. 89(2), pages 825-848, March.
    6. Toru Kitagawa & Aleksey Tetenov, 2018. "Who Should Be Treated? Empirical Welfare Maximization Methods for Treatment Choice," Econometrica, Econometric Society, vol. 86(2), pages 591-616, March.
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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. Toru Kitagawa & Guanyi Wang, 2023. "Individualized Treatment Allocation in Sequential Network Games," Papers 2302.05747, arXiv.org, revised Jul 2023.
    4. Yan Liu, 2022. "Policy Learning under Endogeneity Using Instrumental Variables," Papers 2206.09883, arXiv.org, revised Mar 2024.
    5. 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.
    6. Riccardo D'Adamo, 2021. "Orthogonal Policy Learning Under Ambiguity," Papers 2111.10904, arXiv.org, revised Dec 2022.
    7. 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.
    8. 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.
    9. 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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