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.

中文翻译：

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使用非参数回归模型估计个体治疗效果：综述

大型观测数据越来越多地用于健康、经济和社会科学等学科，在这些学科中，研究人员对因果问题而不是预测感兴趣。在本文中，我们从一项旨在调查参与学校膳食计划对健康指标影响的实证研究开始，研究使用基于非参数回归的方法估计异质治疗效果的问题。首先，我们介绍了与使用观察或非完全随机数据进行因果推理相关的设置和问题，以及如何借助统计学习工具来解决这些问题。然后，我们审查并开发了现有最先进框架的统一分类法，允许通过非参数回归模型进行个体治疗效果估计。在简要概述了模型选择问题之后，我们说明了一些方法在三个不同的模拟研究中的性能。最后，我们通过对学校膳食计划数据的实证分析展示了一些方法的使用。