Ensemble Method for Estimating Individualized Treatment Effects

TL;DR

This paper introduces an ensemble approach for estimating individualized treatment effects, demonstrating its superiority over model selection through empirical results and theoretical guarantees.

Contribution

It proposes a novel ensemble algorithm for treatment effect estimation and provides theoretical proof of its asymptotic optimality.

Findings

Ensembling outperforms model selection on 43 benchmark datasets.

The ensemble model is asymptotically as accurate as the best individual model.

Theoretical proof guarantees ensemble's performance even with many candidate models.

Abstract

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.