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Heterogeneous treatment effect (HTE) estimation is critical in medicalresearch. It provides insights into how treatment effects vary amongindividuals, which can provide statistical evidence for precision medicine.While most existing methods focus on binary treatment situations, real-worldapplications often involve multiple interventions. However, current HTEestimation methods are primarily designed for binary comparisons and often relyon black-box models, which limit their applicability and interpretability inmulti-arm settings. To address these challenges, we propose an interpretablemachine learning framework for HTE estimation in multi-arm trials. Our methodemploys a rule-based ensemble approach consisting of rule generation, ruleensemble, and HTE estimation, ensuring both predictive accuracy andinterpretability. Through extensive simulation studies and real dataapplications, the performance of our method was evaluated againststate-of-the-art multi-arm HTE estimation approaches. The results indicate thatour approach achieved lower bias and higher estimation accuracy compared withthose of existing methods. Furthermore, the interpretability of our frameworkallows clearer insights into how covariates influence treatment effects,facilitating clinical decision making. By bridging the gap between accuracy andinterpretability, our study contributes a valuable tool for multi-arm HTEestimation, supporting precision medicine.

Causal rule ensemble approach for multi-arm data