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Model selection for estimating treatment effects

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  • Craig A. Rolling
  • Yuhong Yang

Abstract

type="main" xml:id="rssb12043-abs-0001"> Researchers often believe that a treatment's effect on a response may be heterogeneous with respect to certain baseline covariates. This is an important premise of personalized medicine. Several methods for estimating heterogeneous treatment effects have been proposed. However, little attention has been given to the problem of choosing between estimators of treatment effects. Models that best estimate the regression function may not be best for estimating the effect of a treatment; therefore, there is a need for model selection methods that are targeted to treatment effect estimation. We demonstrate an application of the focused information criterion in this setting and develop a treatment effect cross-validation aimed at minimizing treatment effect estimation errors. Theoretically, treatment effect cross-validation has a model selection consistency property when the data splitting ratio is properly chosen. Practically, treatment effect cross-validation has the flexibility to compare different types of models. We illustrate the methods by using simulation studies and data from a clinical trial comparing treatments of patients with human immunodeficiency virus.

Suggested Citation

  • Craig A. Rolling & Yuhong Yang, 2014. "Model selection for estimating treatment effects," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 76(4), pages 749-769, September.
    • Handle: RePEc:bla:jorssb:v:76:y:2014:i:4:p:749-769
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    File URL: http://hdl.handle.net/10.1111/rssb.2014.76.issue-4
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    Cited by:

    1. Shi, Pengfei & Zhang, Xinyu & Zhong, Wei, 2024. "Estimating conditional average treatment effects with heteroscedasticity by model averaging and matching," Economics Letters, Elsevier, vol. 238(C).
    2. Necati Ertekin & Jeffrey D. Shulman & Haipeng (Allan) Chen, 2019. "On the Profitability of Stacked Discounts: Identifying Revenue and Cost Effects of Discount Framing," Marketing Science, INFORMS, vol. 38(2), pages 317-342, March.
    3. Kazuhiko Shinoda & Takahiro Hoshino, 2022. "Orthogonal Series Estimation for the Ratio of Conditional Expectation Functions," Papers 2212.13145, arXiv.org.
    4. Zhongqi Liang & Qihua Wang & Yuting Wei, 2022. "Robust model selection with covariables missing at random," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 74(3), pages 539-557, June.
    5. Alicia Curth & Mihaela van der Schaar, 2023. "In Search of Insights, Not Magic Bullets: Towards Demystification of the Model Selection Dilemma in Heterogeneous Treatment Effect Estimation," Papers 2302.02923, arXiv.org, revised Jun 2023.
    6. Hui Lan & Vasilis Syrgkanis, 2023. "Causal Q-Aggregation for CATE Model Selection," Papers 2310.16945, arXiv.org, revised Nov 2023.

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