  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. 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 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.
