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Heterogeneous treatment effects (HTEs) are commonly identified duringrandomized controlled trials (RCTs). Identifying subgroups of patients withsimilar treatment effects is of high interest in clinical research to advanceprecision medicine. Often, multiple clinical outcomes are measured during anRCT, each having a potentially heterogeneous effect. Recently there has beenhigh interest in identifying subgroups from HTEs, however, there has been lessfocus on developing tools in settings where there are multiple outcomes. Inthis work, we propose a framework for partitioning the covariate space toidentify subgroups across multiple outcomes based on the joint CIs. We test ouralgorithm on synthetic and semi-synthetic data where there are two outcomes,and demonstrate that our algorithm is able to capture the HTE in both outcomessimultaneously.

Identifying Heterogeneous Treatment Effects in Multiple Outcomes using Joint Confidence Intervals