论文标题
因果相互作用树：基于树的亚组识别观测数据
Causal Interaction Trees: Tree-Based Subgroup Identification for Observational Data
论文作者
论文摘要
我们提出了因果关系树，用于识别使用观察数据增强治疗效果的参与者的亚组。我们通过使用分裂标准扩展了分类和回归树算法，该标准的重点是基于亚组特异性治疗效应估计量最大化组间治疗效应异质性，以决定算法中的决策。我们得出了三个亚组特异性治疗效应估计值的性能，这些估计值解释了数据的观察性质 - 反概率加权，G形成型和双重稳健估计器。我们在观察性研究中研究了拟议算法的性能，并实施了算法，该研究评估了右心导管对重症患者的有效性。
We propose Causal Interaction Trees for identifying subgroups of participants that have enhanced treatment effects using observational data. We extend the Classification and Regression Tree algorithm by using splitting criteria that focus on maximizing between-group treatment effect heterogeneity based on subgroup-specific treatment effect estimators to dictate decision-making in the algorithm. We derive properties of three subgroup-specific treatment effect estimators that account for the observational nature of the data -- inverse probability weighting, g-formula and doubly robust estimators. We study the performance of the proposed algorithms using simulations and implement the algorithms in an observational study that evaluates the effectiveness of right heart catheterization on critically ill patients.
