Helge Kniep

A comparative study of causal machine learning models for personalized treatment of acute ischemic stroke

Abstract Background Accurate estimation of the individualized treatment effect (ITE) is critical for supporting personalized decision-making in acute ischaemic stroke, while the average treatment effect (ATE) provides a cohort-level benchmark for method comparison and context. Ho...