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.

翻译：我们建议用“Causal Exactive 树”来确定使用观测数据具有强化治疗效果的参与者分组。我们扩展了“分类”和“递减树”算法,采用分拆标准,侧重于最大限度地实现群体间治疗效果异质性,基于分组特定治疗效果的估测器来决定算法中的决策。我们从三个分组特定治疗效果估计器中得出特性,这些估计器考虑到数据的观察性质 -- -- 反概率加权、g-形态和双重强健的估测器。我们利用模拟来研究拟议算法的性能,并在一项评估对重病患者进行右心导导作用的观察研究中应用算法。