Robust inference for risk heterogeneity under group imbalance

ArXi:2606.00797v1 Announce Type: cross Population-level heterogeneity is ubiquitous in biomedical data, where differences across graphic or clinical subgroups can substantially alter risk patterns. For example, in intensive care unit (ICU) studies, the mortality risk associated with specific admission diagnoses can vary across ethnic groups. Existing approaches for detecting risk heterogeneity are often sensitive to baseline model misspecification and regularization bias, both of which commonly arise in practice.