Diagnosing Conformal Prediction Failures Under Distribution Shift: A COVID-19 Case Study
cs.LG, cs.AI, stat.ML
Submitted: 2026-01-01
Updated: 2026-08-27
Comments: Accepted and presented at the 42nd Conference on Uncertainty in Artificial Intelligence (UAI 2026). Published in Proceedings of Machine Learning Research (PMLR), Volume 337, pages 3453-3476, 2026
Journal ref: Proceedings of the 42nd Conference on Uncertainty in Artificial Intelligence, PMLR 337:3453-3476, 2026
License: http://creativecommons.org/licenses/by/4.0/
The gist: Conformal prediction provides distribution-free coverage guarantees, but these degrade under distribution shift - and practitioners lack tools to anticipate which deployed models will fail before
Terminology
Abstract
Conformal prediction provides distribution-free coverage guarantees, but these degrade under distribution shift - and practitioners lack tools to anticipate which deployed models will fail before observing test data. We propose SHapley Additive exPlanations (SHAP) concentration - the fraction of feature importance concentrated in the top feature - as a pre-deployment diagnostic for conformal prediction vulnerability in gradient-boosted classifiers. Using COVID-19 as a naturalistic case study, eight supply chain tasks experience identical temporal shift yet coverage drops ranging from negligible to catastrophic. Feature-importance concentration is strongly associated with failure severity across 16 multiclass tasks in 9 domains, while standard distributional shift detectors detect shift uniformly across tasks but cannot distinguish catastrophic from robust outcomes. External validation across 9 non-supply-chain datasets shows partial transfer. We prove a formal theorem showing that Adaptive Prediction Sets conformity-score bounds worsen monotonically with concentration under explicit assumptions, verified empirically. The diagnostic identifies concentrated-dependence failures characteristic of gradient-boosted models but does not detect global-sensitivity failures observed in neural networks. A decision framework operationalizes the diagnostic as an exploratory pre-deployment rule with an uncertainty band around a concentration threshold.
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