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.

翻译：本研究提出一个基于RuleFit方法的新型框架，用于估计随机临床试验中的异质性处理效应（HTE）。为此，我们在所提出的框架中采用元算法的S学习器。该方法为主效应和处理效应引入规则项，使HTE能够以可解释的规则形式呈现。通过在模型中纳入主效应项，所选规则被表示为排除其他效应的HTE。通过数值模拟验证，该方法展现出与其他集成学习方法相当的性能，并利用实际数据应用展示了所提方法的可解释性。