# Ensemble Method for Estimating Individualized Treatment Effects

arXiv: https://arxiv.org/abs/2202.12445 · HTML: https://mlsanity.com/paper/2202.12445/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 33 references that are in the map.
- First posted on arXiv 25 Feb 2022.

## Builds on (most cited first, 9 of 9)

- [Estimation and Inference of Heterogeneous Treatment Effects using Random Forests](https://mlsanity.com/paper/1510.04342/) (2015)
- [Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learning](https://mlsanity.com/paper/1706.03461/) (2017)
- [Quasi-Oracle Estimation of Heterogeneous Treatment Effects](https://mlsanity.com/paper/1712.04912/) (2017)
- [Learning Representations for Counterfactual Inference](https://mlsanity.com/paper/1605.03661/) (2016)
- [Automated versus do-it-yourself methods for causal inference: Lessons learned from a data analysis competition](https://mlsanity.com/paper/1707.02641/) (2017)
- [Some methods for heterogeneous treatment effect estimation in high-dimensions](https://mlsanity.com/paper/1707.00102/) (2017)
- [A comparison of methods for model selection when estimating individual treatment effects](https://mlsanity.com/paper/1804.05146/) (2018)
- [Synth-Validation: Selecting the Best Causal Inference Method for a Given Dataset](https://mlsanity.com/paper/1711.00083/) (2017)
- [Counterfactual Cross-Validation: Stable Model Selection Procedure for Causal Inference Models](https://mlsanity.com/paper/1909.05299/) (2019)

## Built on by (most cited first, 4 of 4)

- [Empirical Analysis of Model Selection for Heterogeneous Causal Effect Estimation](https://mlsanity.com/paper/2211.01939/) (2022)
- [Causal Q-Aggregation for CATE Model Selection](https://mlsanity.com/paper/2310.16945/) (2023)
- [Cross-World Assumption and Refining Prediction Intervals for Individual Treatment Effects](https://mlsanity.com/paper/2507.12581/) (2025)
- [Robust CATE Estimation Using Novel Ensemble Methods](https://mlsanity.com/paper/2407.03690/) (2024)

## Abstract (arXiv)

In many medical and business applications, researchers are interested in estimating individualized treatment effects using data from a randomized experiment. For example in medical applications, doctors learn the treatment effects from clinical trials and in technology companies, researchers learn them from A/B testing experiments. Although dozens of machine learning models have been proposed for this task, it is challenging to determine which model will be best for the problem at hand because ground-truth treatment effects are unobservable. In contrast to several recent papers proposing methods to select one of these competing models, we propose an algorithm for aggregating the estimates from a diverse library of models. We compare ensembling to model selection on 43 benchmark datasets, and find that ensembling wins almost every time. Theoretically, we prove that our ensemble model is (asymptotically) at least as accurate as the best model under consideration, even if the number of candidate models is allowed to grow with the sample size.