A Practical Upper Bound on Selection Bias Effects in Medical Prediction Models
Selection bias is a common and often unavoidable aspect of real-world data that challenges the generalizability of machine learning models. When models trained on biased data are deployed in the broader target population, poor model generalization may lead to real harm, particularly in high-risk settings such as healthcare. This risk highlights the need for practitioners to reliably assess model generalizability prior to deployment. However, existing methods for predicting model performance rely on unrealistic access to the target distribution or knowledge of the selection mechanism causing bias. To address these limitations, we propose a novel upper bound on the worst-case model performa...
| Authors | Kara Liu, Maggie Wang, Russ B. Altman |
|---|---|
| Primary category | cs.LG |
| Published | 30 May 2026 |
| Last updated at source | 30 May 2026 |
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