Rules Ensemble Method with Group Lasso for Heterogeneous Treatment Effect Estimation

TL;DR

This paper introduces a modified RuleFit-based machine learning method for estimating heterogeneous treatment effects, emphasizing interpretability and accuracy in complex, noisy real-world data for precision medicine.

Contribution

It proposes a novel adaptation of RuleFit for direct HTE estimation, enhancing interpretability and applicability in personalized treatment effect analysis.

Findings

Effective estimation of HTEs with improved interpretability.

Demonstrated accuracy in complex real-world datasets.

Applicable to precision medicine contexts.

Abstract

The increasing scientific attention given to precision medicine based on real-world data has led many recent studies to clarify the relationships between treatment effects and patient characteristics. However, this is challenging because of ubiquitous heterogeneity in the treatment effect for individuals and the real-world data on their background being complex and noisy. Because of their flexibility, various heterogeneous treatment effect (HTE) machine learning (ML) estimation methods have been proposed. However, most ML methods incorporate black-box models that hamper direct interpretation of the interrelationships between individuals' characteristics and the treatments' effects. This study proposes an ML method for estimating HTE based on the rule ensemble method termed RuleFit. The main advantage of RuleFit are interpretability and accuracy. However, HTEs are always defined in the potential outcome framework, and RuleFit cannot be applied directly. Thus, we modified RuleFit and proposed a method to estimate HTEs that directly interpret the interrelationships among the individuals' features from the model.