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Estimating heterogeneous treatment effects versus building individualized treatment rules: Connection and disconnection

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  • Chen, Zhongyuan
  • Xie, Jun

Abstract

Estimating heterogeneous treatment effects is a well studied topic in the statistics literature. More recently, it has regained attention due to an increasing need for precision medicine as well as the increased use of state-of-art machine learning methods in the estimation. Furthermore, estimating heterogeneous treatment effects is directly related to building an individualized treatment rule, which is a decision rule of treatment according to patient characteristics. This paper examines the connection and disconnection between these two research problems. Notably, a better estimation of the heterogeneous treatment effects may or may not lead to a better individualized treatment rule. We provide theoretical frameworks to explain the connection and disconnection and demonstrate two different scenarios through simulations. Our conclusion sheds light on a practical guide that under certain circumstances, there is no need to enhance estimation of the treatment effects, as it does not alter the treatment decision.

Suggested Citation

  • Chen, Zhongyuan & Xie, Jun, 2023. "Estimating heterogeneous treatment effects versus building individualized treatment rules: Connection and disconnection," Statistics & Probability Letters, Elsevier, vol. 199(C).
    • Handle: RePEc:eee:stapro:v:199:y:2023:i:c:s0167715223000780
    DOI: 10.1016/j.spl.2023.109854
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    References listed on IDEAS

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    1. Yingqi Zhao & Donglin Zeng & A. John Rush & Michael R. Kosorok, 2012. "Estimating Individualized Treatment Rules Using Outcome Weighted Learning," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(499), pages 1106-1118, September.
    2. Stefan Wager & Susan Athey, 2018. "Estimation and Inference of Heterogeneous Treatment Effects using Random Forests," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(523), pages 1228-1242, July.
    3. Lin, Yi, 2004. "A note on margin-based loss functions in classification," Statistics & Probability Letters, Elsevier, vol. 68(1), pages 73-82, June.
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