ConformaDecompose: Explaining Uncertainty via Calibration Localization
For users of conformal prediction, this framework enhances interpretability by decomposing uncertainty into reducible and irreducible components, addressing a key limitation of global calibration.
ConformalDecompose introduces a framework to explain the sources of uncertainty in conformal prediction intervals by localizing calibration, revealing how much uncertainty is reducible. Across benchmarks, the method aligns with epistemic proxies and provides instance-level insights without altering coverage.
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.