Causal effect random forest of interaction trees for learning individualized treatment regimes with multiple treatments in observational studies
Causal effect random forest of interaction trees for learning individualized treatment regimes with multiple treatments in observational studies
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DOI：
10.1002/sta4.457
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发表时间：
2022-12-01
期刊：
影响因子：
1.7
通讯作者：
Fan, Juanjuan
中科院分区：
文献类型：
--
作者：
Li, Luo;Levine, Richard A.;Fan, Juanjuan
个体对治疗的反应可能具有显著的异质性。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是针对二元治疗的随机研究设计的。在这项研究中，我们提出了一种算法来扩展随机森林的相互作用树（苏等人，2009年，接受多种治疗。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治疗的随机和观察性研究数据。该方法的性能，相对于现有的方法在文献中，通过模拟研究进行评估。所提出的方法适用于多个志愿教育计划在一所大型公立大学的评估。
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.