用博弈论统一解释各类数据指标变化的原因。
Explaining the "Why": A Unified Framework for the Additive Attribution of Changes in Arbitrary Measures
- 基于博弈论重构归因问题，按数学结构分类指标
- 支持任意指标，可精确或近似计算归因贡献
- 能揭示复杂现象如辛普森悖论的深层原因
解释聚合指标变化的原因是数据智能中的关键挑战，现有系统难以应对。当前方法缺乏同时具备通用性、维度与组合完整性、以及可解释严谨性的统一方案。为此，我们提出一个基于合作博弈论的原理性框架。核心贡献是根据指标的数学结构进行分类，从而支持从通用近似到精确闭式解的算法谱系，在通用性与性能间实现合理权衡。多维度评估验证其优越性：模拟实验确认数值准确性及对非加性指标的泛化能力；辛普森悖论案例研究展示独特可解释性；实际根因分析实验表明其显著优于现有系统。
原文摘要 · Abstract (English)
Explaining why aggregated measures change is a critical challenge in data analytics that existing systems struggle to address. While current attribution methods exist, they lack a unified solution that is simultaneously general for arbitrary measures, holistic across both data dimensions and measure composition, and rigorous in its interpretability. To bridge this gap, we introduce a principled framework that reframes attribution through the powerful lens of cooperative game theory. Our key contribution is a classification of measures based on their mathematical structure, which enables a spectrum of algorithms-from general approximations to exact, closed-form solutions-that offer a principled trade-off between generality and performance. We demonstrate our framework's superiority through a multi-faceted evaluation: simulations first confirm its numerical accuracy and then its generality for non-additive measures; a case study on Simpson's Paradox showcases its unique interpretability; and a final experiment proves its practical utility by significantly outperforming existing root cause analysis systems.
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