Explaining the "Why": A Unified Framework for the Additive Attribution of Changes in Arbitrary Measures

• URL: http://arxiv.org/abs/2604.26266v1
• Date: Wed, 29 Apr 2026 03:49:54 GMT
• Title: Explaining the "Why": A Unified Framework for the Additive Attribution of Changes in Arbitrary Measures
• Abstract summary: We introduce a principled framework that reframes attribution through the powerful lens of cooperative game theory.<n>Our key contribution is a classification of measures based on their mathematical structure.<n>We demonstrate our framework's superiority through a multi-faceted evaluation.
• Score: 9.511180482257737
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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