Treatments ideally mitigate pathogenesis, or the detrimental effects of the root causes of disease. However, existing definitions of treatment effect fail to account for pathogenic mechanism. We therefore introduce the Treated Root causal Effects (TRE) metric which measures the ability of a treatment to modify root causal effects. We leverage TREs to automatically identify treatment targets and cluster patients who respond similarly to treatment. The proposed algorithm learns a partially linear causal model to extract the root causal effects of each variable and then estimates TREs for target discovery and downstream subtyping. We maintain interpretability even without assuming an invertible structural equation model. Experiments across a range of datasets corroborate the generality of the proposed approach.
