论文标题
规则合奏方法与小组拉索进行异质治疗效果估计
Rules Ensemble Method with Group Lasso for Heterogeneous Treatment Effect Estimation
论文作者
论文摘要
基于现实世界数据给予精确医学的科学关注越来越多，导致了许多最近的研究，以阐明治疗效果与患者特征之间的关系。但是，由于治疗效果无处不在，因此这是具有挑战性的，以及对其背景的现实数据复杂而嘈杂。由于它们的灵活性，已经提出了各种异质治疗效果（HTE）机器学习（ML）估计方法。但是，大多数ML方法结合了黑框模型，这些模型会阻碍对个体特征和治疗效应之间相互关系的直接解释。这项研究提出了一种基于规则合规法规则集合方法估算HTE的ML方法。 RuleFit的主要优点是可解释性和准确性。 However, HTEs are always defined in the potential outcome framework, and RuleFit cannot be applied directly.因此，我们修改了规则fit，并提出了一种方法来估计直接解释模型中个体特征之间相互关系的HTE的方法。
The increasing scientific attention given to precision medicine based on real-world data has led many recent studies to clarify 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 background being complex and noisy. 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 interpretation of the interrelationships between individuals' characteristics and the treatments' effects. This study proposes an ML method for estimating HTE based on the rule ensemble method termed RuleFit. The main advantage 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 interrelationships among the individuals' features from the model.
