# Can a calibration metric be both testable and actionable?

arXiv: https://arxiv.org/abs/2502.19851 · HTML: https://mlsanity.com/paper/2502.19851/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 32 references that are in the map.
- First posted on arXiv 27 Feb 2025.

## Builds on (most cited first, 14 of 14)

- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [Measuring Calibration in Deep Learning](https://mlsanity.com/paper/1904.01685/) (2019)
- [Verified Uncertainty Calibration](https://mlsanity.com/paper/1909.10155/) (2019)
- [Multiaccuracy: Black-Box Post-Processing for Fairness in Classification](https://mlsanity.com/paper/1805.12317/) (2018)
- [Mitigating Bias in Calibration Error Estimation](https://mlsanity.com/paper/2012.08668/) (2020)
- [Distribution-free binary classification: prediction sets, confidence intervals and calibration](https://mlsanity.com/paper/2006.10564/) (2020)
- [Smooth ECE: Principled Reliability Diagrams via Kernel Smoothing](https://mlsanity.com/paper/2309.12236/) (2023)
- [U-Calibration: Forecasting for an Unknown Agent](https://mlsanity.com/paper/2307.00168/) (2023)
- [High-Dimensional Prediction for Sequential Decision Making](https://mlsanity.com/paper/2310.17651/) (2023)
- [Near-Optimal Algorithms for Omniprediction](https://mlsanity.com/paper/2501.17205/) (2025)
- [Metrics of calibration for probabilistic predictions](https://mlsanity.com/paper/2205.09680/) (2022)
- [Optimization of Scoring Rules](https://mlsanity.com/paper/2007.02905/) (2020)
- [Forecasting for Swap Regret for All Downstream Agents](https://mlsanity.com/paper/2402.08753/) (2024)
- [Calibration Error for Decision Making](https://mlsanity.com/paper/2404.13503/) (2024)

## Built on by (most cited first, 12 of 12)

- [Calibration through the Lens of Indistinguishability](https://mlsanity.com/paper/2509.02279/) (2025)
- [Efficient Calibration for Decision Making](https://mlsanity.com/paper/2511.13699/) (2025)
- [Sample-efficient Multiclass Calibration under ℓp Error](https://mlsanity.com/paper/2509.23000/) (2025)
- [Scalable Utility-Aware Multiclass Calibration](https://mlsanity.com/paper/2510.25458/) (2025)
- [Computational and Statistical Hardness of Calibration Distance](https://mlsanity.com/paper/2603.18391/) (2026)
- [Clustered Calibration: Representation-Aware Probability Calibration via Learned Subpopulations](https://mlsanity.com/paper/2510.19328/) (2025)
- [Smoothed Elicitation Complexity for Approximate Gamma-calibration of Discrete Classification Tasks](https://mlsanity.com/paper/2605.23017/) (2026)
- [Human-AI Teaming Through the Lens of Calibration](https://mlsanity.com/paper/2606.10906/) (2026)
- [Calibration without labels in multiple testing](https://mlsanity.com/paper/2606.19737/) (2026)
- [An analysis of binary isotonic regression: degrees of freedom and implications for calibration](https://mlsanity.com/paper/2607.27301/) (2026)
- [Sharp Integrality Gaps in Calibration Distance](https://mlsanity.com/paper/2610.05679/) (2026)
- [Noisy-Label Calibration with Multiple Unreliable Labels](https://mlsanity.com/paper/2610.19543/) (2026)

## Abstract (arXiv)

Forecast probabilities often serve as critical inputs for binary decision making. In such settings, calibrationunicodex2014ensuring forecasted probabilities match empirical frequenciesunicodex2014is essential. Although the common notion of Expected Calibration Error (ECE) provides actionable insights for decision making, it is not testable: it cannot be empirically estimated in many practical cases. Conversely, the recently proposed Distance from Calibration (dCE) is testable, but it is not actionable since it lacks decision-theoretic guarantees needed for high-stakes applications. To resolve this question, we consider Cutoff Calibration Error, a calibration measure that bridges this gap by assessing calibration over intervals of forecasted probabilities. We show that Cutoff Calibration Error is both testable and actionable, and we examine its implications for popular post-hoc calibration methods, such as isotonic regression and Platt scaling.