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This research introduces a novel diagnostic tool using SHAP concentration to predict when conformal prediction methods will fail under distribution shift, specifically within gradient-boosted classifiers. By analyzing feature importance distribution rather than just detecting the presence of a shift, the study distinguishes between robust and catastrophic coverage drops in real-world scenarios like supply chain forecasting during a pandemic. The findings provide a formal theorem and empirical validation that help practitioners anticipate model vulnerabilities before deployment, addressing a critical gap in reliable uncertainty quantification.
Diagnosing Conformal Prediction Failures Under Distribution Shift: A COVID-19 Case Study
arXiv:2601.00908v2 Announce Type: replace-cross 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.
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.
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
Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
arXiv:2601.00908 [cs.LG]
(or arXiv:2601.00908v2 [cs.LG] for this version)
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