改进了数据与特征贡献度评估方法，能处理优先级依赖关系。
Priority-Aware Shapley Value
- 引入硬约束和软权重的优先级机制，突破传统Shapley值假设。
- 在MNIST/CIFAR10上实现更符合结构的数据估值，Census Income特征分配更合理。
- 适合需要考虑数据重用或信任等级的场景，如医疗、金融建模。
Shapley值广泛用于模型无关的数据估值与特征归因，但其隐含假设是贡献者可互换。当贡献者存在依赖关系（如数据复用/增强或因果特征顺序）或需按信任、风险等调整贡献时，此假设可能失效。本文提出优先级感知的Shapley值（PASV），同时融入硬性优先级约束与贡献者特定的软权重。PASV适用于一般优先级结构，可退化为仅含优先级或仅含权重的特殊情形，且由自然公理唯一刻画。我们设计了高效的相邻交换Metropolis-Hastings采样器，支持大规模蒙特卡洛估计，并分析了极端权重下的渐近行为。在数据估值（MNIST/CIFAR10）和特征归因（Census Income）任务上的实验表明，PASV能实现更符合结构的分配结果，并通过提出的“优先级扫掠”实现实用的敏感性分析。
原文摘要 · Abstract (English)
Shapley values are widely used for model-agnostic data valuation and feature attribution, yet they implicitly assume contributors are interchangeable. This can be problematic when contributors are dependent (e.g., reused/augmented data or causal feature orderings) or when contributions should be adjusted by factors such as trust or risk. We propose Priority-Aware Shapley Value (PASV), which incorporates both hard precedence constraints and soft, contributor-specific priority weights. PASV is applicable to general precedence structures, recovers precedence-only and weight-only Shapley variants as special cases, and is uniquely characterized by natural axioms. We develop an efficient adjacent-swap Metropolis-Hastings sampler for scalable Monte Carlo estimation and analyze limiting regimes induced by extreme priority weights. Experiments on data valuation (MNIST/CIFAR10) and feature attribution (Census Income) demonstrate more structure-faithful allocations and a practical sensitivity analysis via our proposed "priority sweeping".
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