ConformaDecompose: Explaining Uncertainty via Calibration Localization

2026-04-29

AI Summary

This work proposes an uncertainty-aware, interpretable framework for conformal prediction that addresses the limitations of traditional approaches relying on global calibration thresholds, which fail to distinguish instance-level sources of uncertainty or explain variations in prediction interval width. By introducing a localized calibration mechanism—applied progressively during the calibration process—the method enables instance-level diagnosis of reducible uncertainty in regression tasks. Integrating uncertainty decomposition with interpretability analysis, the framework uncovers task-specific patterns of epistemic uncertainty otherwise obscured by interval width. Experiments across multiple benchmark and real-world datasets demonstrate strong alignment between reducible uncertainty and proxy metrics of epistemic uncertainty, while quantifying how their relative contributions vary across tasks.

Abstract

Conformal Prediction provides distribution-free prediction intervals with guaranteed coverage, but its reliance on a single global calibration threshold obscures the sources of uncertainty at the instance level. In particular, it conflates irreducible noise with uncertainty induced by heterogeneous training data (aleatoric), model limitations, or calibration mismatch (epistemic), offering little insight into why an interval is wide or whether it could be reduced. We introduce an uncertainty-aware explainability framework that analyses the reducibility of calibration-induced epistemic conformal uncertainty via progressive calibration localisation for regression tasks. The approach is diagnostic rather than causal: it does not estimate true aleatoric or epistemic uncertainty, but explains how conformal intervals contract and stabilise as calibration support is localised around a test instance. Across benchmarks and real-world data, absolute reducible uncertainty aligns with epistemic proxies, while its relative contribution varies by task, revealing regimes hidden by interval width. This instance-level view complements conformal uncertainty, enhancing interpretability without altering the predictor or coverage.

Problem

• Conformal Prediction
• Uncertainty Explanation
• Calibration Localization
• Aleatoric Uncertainty
• Epistemic Uncertainty

Innovation

• Conformal Prediction
• Uncertainty Explainability
• Calibration Localization
• Epistemic Uncertainty
• Regression