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Estimating Individual Treatment Effects using Non-Parametric Regression Models: a Review

Caron, A; Manolopoulou, I; Baio, G; (2022) Estimating Individual Treatment Effects using Non-Parametric Regression Models: a Review. Journal of the Royal Statistical Society: Series A (Statistics in Society) 10.1111/rssa.12824. (In press). Green open access

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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 investigate the problem of estimating heterogeneous treatment effects using non-parametric regression-based methods. Firstly, 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 provide a review of state-of-the-art methods, with a particular focus on non-parametric modeling, and we cast them under a unifying taxonomy. 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 and on a real world example to investigate the effect of participation in school meal programs on health indicators.

Type: Article
Title: Estimating Individual Treatment Effects using Non-Parametric Regression Models: a Review
Open access status: An open access version is available from UCL Discovery
DOI: 10.1111/rssa.12824
Publisher version: https://doi.org/10.1111/rssa.12824
Language: English
Additional information: Copyright © 2022 The Authors. This is an open access article under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Keywords: Bayesian Non-Parametrics, Causal inference, Heterogeneous Treatment Effects, Machine Learning, Observational Studies, Regression Trees.
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science
URI: https://discovery.ucl.ac.uk/id/eprint/10110969
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