A Guide to Estimating Conditional Average Treatment Effects in Competing Risks Settings
Conditional average treatment effects (CATEs) are central to treatment decision-making in personalized medicine. In competing risks settings, estimating CATEs from survival data allows for patient-specific assessments of treatment effectiveness for a specific event of interest while properly accounting for alternative event types. This distinction is essential in the presence of comorbidities, where competing causes of death may otherwise confound the therapeutic benefit. Focusing on right-censored survival times with binary treatment, we examine CATEs defined as covariate-conditional differences in the absolute risk for the event of interest at a fixed time. To this end, we study meta-le...
| Authors | Daniel Klippert, Sarah Friedrich, Markus Pauly |
|---|---|
| Primary category | stat.AP |
| Published | 8 June 2026 |
| Last updated at source | 8 June 2026 |
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