Rule ensemble method with adaptive group lasso for heterogeneous treatment effect estimation

The increasing scientific attention given to precision medicine based on real-world data has led to many recent studies clarifying 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 backgrounds being complex and noisy. Because of their flexibility, various machine learning (ML) methods have been proposed for estimating heterogeneous treatment effect (HTE). However, most ML methods incorporate black-box models that hamper direct interpretation of the relationships between an individual's characteristics and treatment effects. This study proposes an ML method for estimating HTE based on the rule ensemble method RuleFit. The main advantages 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 relationships among the individuals' features from the model. Actual data from an HIV study, the ACTG 175 dataset, was used to illustrate the interpretation based on the ensemble of rules created by the proposed method. The numerical results confirm that the proposed method has high prediction accuracy compared to previous methods, indicating that the proposed method establishes an interpretable model with sufficient prediction accuracy.