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Estimating the Individual Treatment Effect with Different Treatment Group Sizes

Author

Listed:
  • Luyuan Song

    (School of Mathematical Sciences, University of Electronic Science and Technology of China, Chengdu 611731, China)

  • Xiaojun Zhang

    (School of Mathematical Sciences, University of Electronic Science and Technology of China, Chengdu 611731, China)

Abstract

Machine learning for causal inference, particularly at the individual level, has attracted intense interest in many domains. Existing techniques focus on controlling differences in distribution between treatment groups in a data-driven manner, eliminating the effects of confounding factors. However, few of the current methods adequately discuss the difference in treatment group sizes. Two approaches, a direct and an indirect one, deal with potential missing data for estimating individual treatment with binary treatments and different treatment group sizes. We embed the two methods into certain frameworks based on the domain adaption and representation. We validate the performance of our method by two benchmarks in the causal inference community: simulated data and real-world data. Experiment results verify that our methods perform well.

Suggested Citation

  • Luyuan Song & Xiaojun Zhang, 2024. "Estimating the Individual Treatment Effect with Different Treatment Group Sizes," Mathematics, MDPI, vol. 12(8), pages 1-17, April.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:8:p:1224-:d:1378514
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    References listed on IDEAS

    as
    1. Michael C. Knaus, 2021. "A double machine learning approach to estimate the effects of musical practice on student’s skills," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(1), pages 282-300, January.
    2. Michael C Knaus, 2022. "Double machine learning-based programme evaluation under unconfoundedness [Econometric methods for program evaluation]," The Econometrics Journal, Royal Economic Society, vol. 25(3), pages 602-627.
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