Multi-Study Causal Forest (MCF): A flexible framework for data borrowing in the presence of varying treatment effect heterogeneity
多研究因果森林:处理效应异质性变化下的数据借用框架
Tailoring treatment assignment to specific individuals can improve the health outcomes, but a single study may offer inadequate information for this purpose. The ability to leverage information from an auxiliary data source deemed to be `most similar' to a primary data source has been shown to improve estimates of treatment effects. In this paper, we introduce a framework, the Multi-Study Causal Forest (MCF), to borrow individual patient-level data from an auxiliary data…