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

TL;DR

This paper reviews non-parametric regression methods for estimating individual treatment effects in observational studies, highlighting their application, challenges, and performance through simulations and a real-world case study on school meal programs.

Contribution

It provides a comprehensive taxonomy of existing non-parametric methods for causal effect estimation and demonstrates their application and effectiveness through simulations and empirical data.

Findings

Non-parametric methods can effectively estimate heterogeneous treatment effects.

Model selection is crucial for accurate causal inference.

Methods show promising results in both simulated and real-world data.

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. 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.