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

arXiv: https://arxiv.org/abs/2009.06472 · HTML: https://mlsanity.com/paper/2009.06472/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Sep 2020.

## Built on by (most cited first, 20 of 20)

- [Causal Effect Inference for Structured Treatments](https://mlsanity.com/paper/2106.01939/) (2021)
- [Comparison of meta-learners for estimating multi-valued treatment heterogeneous effects](https://mlsanity.com/paper/2205.14714/) (2022)
- [β-Intact-VAE: Identifying and Estimating Causal Effects under Limited Overlap](https://mlsanity.com/paper/2110.05225/) (2021)
- [Shrinkage Bayesian Causal Forests for Heterogeneous Treatment Effects Estimation](https://mlsanity.com/paper/2102.06573/) (2021)
- [Heterogeneous Treatment Effect with Trained Kernels of the Nadaraya-Watson Regression](https://mlsanity.com/paper/2207.09139/) (2022)
- [Interpretable Deep Causal Learning for Moderation Effects](https://mlsanity.com/paper/2206.10261/) (2022)
- [Counterfactual Learning with Multioutput Deep Kernels](https://mlsanity.com/paper/2211.11119/) (2022)
- [Bayesian Causal Forests for Multivariate Outcomes: Application to Irish Data From an International Large Scale Education Assessment](https://mlsanity.com/paper/2303.04874/) (2023)
- [A flexible Bayesian g-formula for causal survival analyses with time-dependent confounding](https://mlsanity.com/paper/2402.02306/) (2024)
- [Extended Fiducial Inference for Individual Treatment Effects via Deep Neural Networks](https://mlsanity.com/paper/2505.01995/) (2025)
- [Horseshoe Forests for High-Dimensional Causal Survival Analysis](https://mlsanity.com/paper/2507.22004/) (2025)
- [A Relative Error-Based Evaluation Framework of Heterogeneous Treatment Effect Estimators](https://mlsanity.com/paper/2510.16419/) (2025)
- [A Doubly Robust Machine Learning Approach for Disentangling Treatment Effect Heterogeneity with Functional Outcomes](https://mlsanity.com/paper/2602.11118/) (2026)
- [Orthogonal Series Estimation for the Ratio of Conditional Expectation Functions](https://mlsanity.com/paper/2212.13145/) (2022)
- [Causal prediction models for medication safety monitoring: The diagnosis of vancomycin-induced acute kidney injury](https://mlsanity.com/paper/2311.09137/) (2023)
- [Educational Effects in Mathematics: Conditional Average Treatment Effect depending on the Number of Treatments](https://mlsanity.com/paper/2411.01498/) (2024)
- [Testing Generalizability in Causal Inference](https://mlsanity.com/paper/2411.03021/) (2024)
- [Assessing Surrogate Heterogeneity in Real World Data Using Meta-Learners](https://mlsanity.com/paper/2504.15386/) (2025)
- [Identification and Estimation of Long-Term Treatment Effects with Monotone Missing](https://mlsanity.com/paper/2504.19527/) (2025)
- [Surv-IPTB: An Attention-Based Model for Estimating Individual Probability of Treatment Benefit with Survival Data](https://mlsanity.com/paper/2608.06288/) (2026)

## Abstract (arXiv)

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.