用博弈论方法量化特征对预测不确定性的贡献，提升高风险场景可信度。
Unveil Sources of Uncertainty: Feature Contribution to Conformal Prediction Intervals
- 基于博弈论和置信区间设计特征不确定性归因机制
- 提出比例谢帕利值，按特征重要性分配不确定性贡献
- 适用于医疗、金融等需解释不确定性的高风险应用
合作博弈论方法（如谢帕利值）显著提升了机器学习的可解释性，但现有可解释AI框架主要关注平均模型预测，忽略了预测不确定性。本文提出一种新型、模型无关的不确定性归因（UA）方法，基于共形预测（CP）。通过将CP区间的宽度、上下界等属性定义为博弈的价值函数，系统性地将预测不确定性归因于输入特征。突破传统谢帕利值，采用更丰富的哈桑尼分配方法，特别是比例谢帕利值，使归因结果与特征重要性成比例。提出一种蒙特卡洛近似方法，具备稳健的统计保证，显著提升计算效率。在合成基准和真实数据集上的全面实验验证了该方法的实用性和解释深度。结合合作博弈论与共形预测，提供了一套严谨、灵活的工具，用于理解并沟通高风险机器学习应用中的预测不确定性。
原文摘要 · Abstract (English)
Cooperative game theory methods, notably Shapley values, have significantly enhanced machine learning (ML) interpretability. However, existing explainable AI (XAI) frameworks mainly attribute average model predictions, overlooking predictive uncertainty. This work addresses that gap by proposing a novel, model-agnostic uncertainty attribution (UA) method grounded in conformal prediction (CP). By defining cooperative games where CP interval properties-such as width and bounds-serve as value functions, we systematically attribute predictive uncertainty to input features. Extending beyond the traditional Shapley values, we use the richer class of Harsanyi allocations, and in particular the proportional Shapley values, which distribute attribution proportionally to feature importance. We propose a Monte Carlo approximation method with robust statistical guarantees to address computational feasibility, significantly improving runtime efficiency. Our comprehensive experiments on synthetic benchmarks and real-world datasets demonstrate the practical utility and interpretative depth of our approach. By combining cooperative game theory and conformal prediction, we offer a rigorous, flexible toolkit for understanding and communicating predictive uncertainty in high-stakes ML applications.
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