Individuals may respond to treatments with significant heterogeneity. To optimize the treatment effect, it is necessary to recommend treatments based on individual characteristics. Existing methods in the literature for learning individualized treatment regimes are usually designed for randomized studies with binary treatments. In this study, we propose an algorithm to extend random forest of interaction trees (Su et al., 2009) to accommodate multiple treatments. By integrating the generalized propensity score into the interaction tree growing process, the proposed method can handle both randomized and observational study data with multiple treatments. The performance of the proposed method, relative to existing approaches in the literature, is evaluated through simulation studies. The proposed method is applied to an assessment of multiple voluntary educational programmes at a large public university.

中文翻译：

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交互树的因果效应随机森林，用于在观察性研究中学习具有多种治疗的个体化治疗方案

个体对治疗的反应可能具有显着的异质性。为了优化治疗效果，有必要根据个体特征推荐治疗方法。文献中用于学习个体化治疗方案的现有方法通常设计用于具有二元治疗的随机研究。在这项研究中，我们提出了一种算法来扩展交互树的随机森林 (Su et al., 2009) 以适应多种处理。通过将广义倾向得分集成到交互树生长过程中，所提出的方法可以处理具有多种处理的随机和观察性研究数据。所提出的方法的性能，相对于文献中的现有方法，通过模拟研究进行评估。