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Rejoinder: Learning Optimal Distributionally Robust Individualized Treatment Rules

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  • Weibin Mo
  • Zhengling Qi
  • Yufeng Liu

Abstract

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Suggested Citation

  • Weibin Mo & Zhengling Qi & Yufeng Liu, 2021. "Rejoinder: Learning Optimal Distributionally Robust Individualized Treatment Rules," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 116(534), pages 699-707, April.
    • Handle: RePEc:taf:jnlasa:v:116:y:2021:i:534:p:699-707
    DOI: 10.1080/01621459.2020.1866581
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    Cited by:

    1. Christopher Adjaho & Timothy Christensen, 2022. "Externally Valid Policy Choice," Papers 2205.05561, arXiv.org, revised Jul 2023.
    2. Kohei Yata, 2021. "Optimal Decision Rules Under Partial Identification," Papers 2111.04926, arXiv.org, revised Aug 2023.
    3. Pan Zhao & Yifan Cui, 2023. "A Semiparametric Instrumented Difference-in-Differences Approach to Policy Learning," Papers 2310.09545, arXiv.org.
    4. Daido Kido, 2022. "Distributionally Robust Policy Learning with Wasserstein Distance," Papers 2205.04637, arXiv.org, revised Aug 2022.
    5. Daido Kido, 2023. "Locally Asymptotically Minimax Statistical Treatment Rules Under Partial Identification," Papers 2311.08958, arXiv.org.
    6. Gao, Yuhe & Shi, Chengchun & Song, Rui, 2023. "Deep spectral Q-learning with application to mobile health," LSE Research Online Documents on Economics 119445, London School of Economics and Political Science, LSE Library.

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