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This paper introduces a unified framework for attributing changes in arbitrary aggregated measures by classifying measures based on their mathematical structure and leveraging cooperative game theory. It provides a spectrum of attribution algorithms, ranging from general approximations to exact solutions, trading off generality and performance. Empirical evaluations demonstrate the framework's accuracy, interpretability (via a Simpson's Paradox case study), and practical utility, outperforming existing root cause analysis systems.
Uncover the hidden drivers behind your KPIs: a new attribution framework finally explains *why* your metrics move, not just *what* changed.
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.
