A Practical Upper Bound on Selection Bias Effects in Medical Prediction Models

Researchers propose a novel upper bound method to assess how selection bias in training data impacts machine learning model performance when deployed to broader populations, addressing a critical gap in healthcare AI safety. The approach works with realistic constraints where the selection mechanism and target population are only partially observable, validated through synthetic and real-world medical datasets.

Selection bias in machine learning represents a fundamental challenge for real-world deployment, particularly in high-stakes healthcare environments where model failures directly harm patients. This research tackles a practical problem that existing methods cannot adequately address: predicting performance degradation when models trained on biased datasets encounter new populations. Traditional approaches require unrealistic access to complete target distribution data or full knowledge of bias mechanisms—luxuries rarely available in clinical settings. The proposed upper bound framework operates under realistic constraints where practitioners have incomplete information about both the selection process and target population characteristics, making it immediately applicable to current healthcare workflows. The researchers validate their methodology across three increasingly realistic scenarios: fully synthetic data providing controlled conditions, semi-synthetic data derived from the All of Us Research Program representing diverse population samples, and real-world bias documented in MIMIC-IV clinical records. This tiered validation approach demonstrates both theoretical soundness and practical utility. The work's significance extends beyond healthcare to any domain deploying predictive models across population shifts, including financial systems and criminal justice applications. By enabling practitioners to quantify worst-case performance scenarios before deployment, the framework reduces risks associated with undetected model degradation. The method essentially provides a safety guardrail for machine learning practitioners operating under information constraints typical of real-world implementations. Healthcare institutions and AI developers can leverage this tool to build more robust, generalizable systems while maintaining transparency about model limitations in new populations.

• →A new upper bound method quantifies worst-case model performance degradation under selection bias without requiring full knowledge of the selection mechanism or target distribution.
• →The approach works with partial information about both the bias source and target population, matching realistic deployment constraints in healthcare settings.
• →Validation across synthetic, semi-synthetic, and real-world medical datasets confirms both theoretical validity and practical applicability of the framework.
• →The framework enables safer model deployment in healthcare by allowing practitioners to assess generalizability risks prior to clinical use.
• →The method's principles extend beyond healthcare to any domain where models must perform reliably across different populations.