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Rejoinder: Optimal Individualized Decision Rules Using Instrumental Variable Methods

Author

Listed:
  • Hongxiang Qiu
  • Marco Carone
  • Ekaterina Sadikova
  • Maria Petukhova
  • Ronald C. Kessler
  • Alex Luedtke

Abstract

No abstract is available for this item.

Suggested Citation

  • 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.
    • Handle: RePEc:taf:jnlasa:v:116:y:2021:i:533:p:207-209
    DOI: 10.1080/01621459.2020.1865166
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    Cited by:

    1. Zhou, Yunzhe & Qi, Zhengling & Shi, Chengchun & Li, Lexin, 2023. "Optimizing pessimism in dynamic treatment regimes: a Bayesian learning approach," LSE Research Online Documents on Economics 118233, London School of Economics and Political Science, LSE Library.
    2. Ashesh Rambachan & Amanda Coston & Edward Kennedy, 2022. "Robust Design and Evaluation of Predictive Algorithms under Unobserved Confounding," Papers 2212.09844, arXiv.org, revised May 2024.
    3. Michael Lechner, 2023. "Causal Machine Learning and its use for public policy," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 159(1), pages 1-15, December.
    4. Cui, Yifan & Tchetgen Tchetgen, Eric, 2021. "On a necessary and sufficient identification condition of optimal treatment regimes with an instrumental variable," Statistics & Probability Letters, Elsevier, vol. 178(C).
    5. 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.

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