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The increasing scientific attention given to precision medicine based onreal-world data has led many recent studies to clarify the relationshipsbetween treatment effects and patient characteristics. However, this ischallenging because of ubiquitous heterogeneity in the treatment effect forindividuals and the real-world data on their background being complex andnoisy. 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 interpretationof the interrelationships between individuals' characteristics and thetreatments' effects. This study proposes an ML method for estimating HTE basedon the rule ensemble method termed RuleFit. The main advantage of RuleFit areinterpretability and accuracy. However, HTEs are always defined in thepotential outcome framework, and RuleFit cannot be applied directly. Thus, wemodified RuleFit and proposed a method to estimate HTEs that directly interpretthe interrelationships among the individuals' features from the model.

Rules Ensemble Method with Group Lasso for Heterogeneous Treatment Effect Estimation