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Estimating individual treatment effects using non‐parametric regression models: A review

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  • Alberto Caron
  • Gianluca Baio
  • Ioanna Manolopoulou

Abstract

Large observational data are increasingly available in disciplines such as health, economic and social sciences, where researchers are interested in causal questions rather than prediction. In this paper, we examine the problem of estimating heterogeneous treatment effects using non‐parametric regression‐based methods, starting from an empirical study aimed at investigating the effect of participation in school meal programs on health indicators. First, we introduce the setup and the issues related to conducting causal inference with observational or non‐fully randomized data, and how these issues can be tackled with the help of statistical learning tools. Then, we review and develop a unifying taxonomy of the existing state‐of‐the‐art frameworks that allow for individual treatment effects estimation via non‐parametric regression models. After presenting a brief overview on the problem of model selection, we illustrate the performance of some of the methods on three different simulated studies. We conclude by demonstrating the use of some of the methods on an empirical analysis of the school meal program data.

Suggested Citation

  • Alberto Caron & Gianluca Baio & Ioanna Manolopoulou, 2022. "Estimating individual treatment effects using non‐parametric regression models: A review," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(3), pages 1115-1149, July.
    • Handle: RePEc:bla:jorssa:v:185:y:2022:i:3:p:1115-1149
    DOI: 10.1111/rssa.12824
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    References listed on IDEAS

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    Cited by:

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    5. Michael Lechner & Jana Mareckova, 2024. "Comprehensive Causal Machine Learning," Papers 2405.10198, arXiv.org, revised Feb 2025.

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