Conformal scores improve uncertainty estimates for black-box predictors
Conformal Robustness in Prediction-Driven Decision-Making
Machine Learning
Summary
Prediction tools often give only one best guess without showing how unsure they are, making it hard to make safe decisions afterward. The authors introduce a method that adds a reliable uncertainty measure to any prediction, helping decision-makers understand the risk of being wrong. This method uses a special score that can adjust how robust or cautious the decisions are, linking safety levels with what targets are acceptable. They tested their approach with simulations and a real grocery inventory example, showing it can make operations more reliable and cost-effective.
What this means in practice
- •For inventory managers: Use calibrated uncertainty scores to improve stock decisions with demand forecasts and reduce costs in retail supply chains.
- •For financial risk teams: Integrate conformal scores with existing black-box models to better quantify risks and balance between reliability and target goals.
Authors
Lingjie Zhao, Hansheng Jiang, Wei Qi
Abstract
Modern prediction-driven decision systems often rely on black-box predictors, but a point forecast alone does not provide the uncertainty scale required for robust downstream decision-making. We build a score-calibrated robustness framework that converts any fixed point predictor into a decision-relevant uncertainty representation through distribution-free conformal calibration. We use the conformal score, rather than a particular uncertainty set, as the primitive unit of robustness. The same score determines coverage-calibrated uncertainty sets for reliability-based robust optimization and normalizes target violations in a target-oriented formulation, Conformal Robust Satisficing. This formulation induces a conformal fragility measure that quantifies how rapidly performance deteriorates as the realized parameter departs from the forecast on the conformal score scale. For objective-uncertainty problems under standard convexity and duality conditions, we show that the reliability-based and target-oriented formulations parameterize the same score-calibrated robust decision frontier. This equivalence yields a data-driven mapping between reliability levels and acceptable targets and characterizes the marginal cost of robustness. Synthetic experiments validate the theoretical guarantees and illustrate the reliability-target correspondence. A real-data online-grocery case study demonstrates how the interface combines deep-learning demand forecasts with tractable inventory optimization, thereby improving reliability and reducing operational costs. Overall, our work shows that conformal scores endow fixed black-box predictors with an interpretable uncertainty scale for downstream decision-making while enabling reliability guarantees, acceptable-target selection, and fragility analysis within a unified framework.