Causal rule ensemble method for estimating heterogeneous treatment effect with consideration of main effects

TL;DR

This paper introduces a rule-based ensemble method for estimating heterogeneous treatment effects that emphasizes interpretability by explicitly modeling main effects and treatment effects separately.

Contribution

It extends the RuleFit framework with a novel S-learner approach to produce interpretable rules for HTE estimation, incorporating main effects explicitly.

Findings

Performance comparable to existing ensemble methods in simulations

Provides interpretable rules for treatment effects

Demonstrates applicability with real clinical trial data

Abstract

This study proposes a novel framework based on the RuleFit method to estimate Heterogeneous Treatment Effect (HTE) in a randomized clinical trial. To achieve this, we adopted S-learner of the metaalgorithm for our proposed framework. The proposed method incorporates a rule term for the main effect and treatment effect, which allows HTE to be interpretable form of rule. By including a main effect term in the proposed model, the selected rule is represented as an HTE that excludes other effects. We confirmed a performance equivalent to that of another ensemble learning methods through numerical simulation and demonstrated the interpretation of the proposed method from a real data application.