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.
翻译:在卫生、经济和社会科学等学科中,研究人员对因果关系问题感兴趣,而不是预测,因此越来越多地可以获得大量观测数据。在本文件中,我们调查使用非参数回归法估计不同治疗影响的问题。首先,我们介绍设置和与用观察或非完全随机的数据进行因果关系有关的问题,以及如何利用统计学习工具解决这些问题。然后,我们审查最新方法,特别侧重于非参数模型,并把它们置于统一的分类法之下。在简要概述模型选择问题之后,我们介绍了三种不同的模拟研究和一个真实世界实例,以调查参加学校膳食方案对健康指标的影响。