We introduce causal interaction tree (CIT) algorithms for finding subgroups of individuals with heterogeneous treatment effects in observational data. The CIT algorithms are extensions of the classification and regression tree algorithm that use splitting criteria based on subgroup-specific treatment effect estimators appropriate for observational data. We describe inverse probability weighting, g-formula, and doubly robust estimators of subgroup-specific treatment effects, derive their asymptotic properties, and use them to construct splitting criteria for the CIT algorithms. We study the performance of the algorithms in simulations and implement them to analyze data from an observational study that evaluated the effectiveness of right heart catheterization for critically ill patients.

中文翻译：

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因果交互树：在观察数据中寻找具有异质治疗效果的亚组

我们引入了因果交互树 (CIT) 算法，用于在观察数据中寻找具有异质治疗效果的个体亚组。CIT 算法是分类和回归树算法的扩展，它使用基于适合观察数据的亚组特定治疗效果估计量的分割标准。我们描述了逆概率加权、g 公式和亚组特定治疗效果的双重稳健估计量，推导出它们的渐近特性，并使用它们来构建 CIT 算法的分裂标准。我们研究了算法在模拟中的性能，并实施它们以分析来自一项观察性研究的数据，该研究评估了危重患者右心导管插入术的有效性。