Explaining the "Why": A Unified Framework for the Additive Attribution of Changes in Arbitrary Measures
This work addresses the need for a general, holistic, and rigorous attribution method for data analytics, offering a principled trade-off between generality and performance.
The paper introduces a unified framework for attributing changes in arbitrary aggregated measures, using cooperative game theory to provide interpretable explanations. It outperforms existing root cause analysis systems in practical experiments.
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.