Estimating heterogeneous treatment effects by W-MCM based on Robust reduced rank regression

TL;DR

This paper introduces a robust reduced-rank regression method for estimating heterogeneous treatment effects across multiple outcomes, effectively handling correlations and outliers to identify effective subgroups in personalized medicine.

Contribution

It proposes a novel W-MCM based approach that extends treatment effect estimation to multiple outcomes with robustness and low-rank structure considerations.

Findings

Accurately estimates treatment effects with the proposed method.

Effectively identifies subgroups with significant treatment responses.

Demonstrates robustness to outliers and high outcome correlations.

Abstract

Recently, from the personalized medicine perspective, there has been an increased demand to identify subgroups of subjects for whom treatment is effective. Consequently, the estimation of heterogeneous treatment effects (HTE) has been attracting attention. While various estimation methods have been developed for a single outcome, there are still limited approaches for estimating HTE for multiple outcomes. Accurately estimating HTE remains a challenge especially for datasets where there is a high correlation between outcomes or the presence of outliers. Therefore, this study proposes a method that uses a robust reduced-rank regression framework to estimate treatment effects and identify effective subgroups. This approach allows the consideration of correlations between treatment effects and the estimation of treatment effects with an accurate low-rank structure. It also provides robust estimates for outliers. This study demonstrates that, when treatment effects are estimated using the reduced rank regression framework with an appropriate rank, the expected value of the estimator equals the treatment effect. Finally, we illustrate the effectiveness and interpretability of the proposed method through simulations and real data examples.