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Individualized treatment rules under stochastic treatment cost constraints

Author

Listed:
  • Qiu Hongxiang

    (Department of Statistics, the Wharton School, University of Pennsylvania, Philadelphia, PA 19104, United States)

  • Carone Marco

    (Department of Biostatistics, University of Washington, Seattle, Washington, United States)

  • Luedtke Alex

    (Department of Statistics, University of Washington, Seattle, Washington, United States)

Abstract

Estimation and evaluation of individualized treatment rules have been studied extensively, but real-world treatment resource constraints have received limited attention in existing methods. We investigate a setting in which treatment is intervened upon based on covariates to optimize the mean counterfactual outcome under treatment cost constraints when the treatment cost is random. In a particularly interesting special case, an instrumental variable corresponding to encouragement to treatment is intervened upon with constraints on the proportion receiving treatment. For such settings, we first develop a method to estimate optimal individualized treatment rules. We further construct an asymptotically efficient plug-in estimator of the corresponding average treatment effect relative to a given reference rule.

Suggested Citation

  • 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.
    • Handle: RePEc:bpj:causin:v:10:y:2022:i:1:p:480-493:n:1
    DOI: 10.1515/jci-2022-0005
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    References listed on IDEAS

    as
    1. Hongxiang Qiu & Marco Carone & Ekaterina Sadikova & Maria Petukhova & Ronald C. Kessler & Alex Luedtke, 2021. "Optimal Individualized Decision Rules Using Instrumental Variable Methods," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 116(533), pages 174-191, March.
    2. Hongxiang Qiu & Marco Carone & Ekaterina Sadikova & Maria Petukhova & Ronald C. Kessler & Alex Luedtke, 2021. "Rejoinder: Optimal Individualized Decision Rules Using Instrumental Variable Methods," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 116(533), pages 207-209, March.
    3. Liyang Sun, 2021. "Empirical Welfare Maximization with Constraints," Papers 2103.15298, arXiv.org, revised Sep 2024.
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    Citations

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    Cited by:

    1. Daniel Ngo & Keegan Harris & Anish Agarwal & Vasilis Syrgkanis & Zhiwei Steven Wu, 2023. "Incentive-Aware Synthetic Control: Accurate Counterfactual Estimation via Incentivized Exploration," Papers 2312.16307, arXiv.org, revised Feb 2024.

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