The Weighted Möbius Score: A Unified Framework for Feature Attribution
Yifan Jiang Shane Steinert-Threlkeld
Feature attribution aims to explain the reasoning behind a black-box model's
prediction by identifying the impact of each feature on the prediction. Recent
work has extended feature attribution to interactions between multiple
features. However, the lack of a unified framework has led to a proliferation
of methods that are often not directly comparable. This paper introduces a
parameterized attribution framework -- the Weighted M\"obius Score -- and (i)
shows that many different attribution methods for both individual features and
feature interactions are special cases and (ii) identifies some new methods. By
studying the vector space of attribution methods, our framework utilizes
standard linear algebra tools and provides interpretations in various fields,
including cooperative game theory and causal mediation analysis. We empirically
demonstrate the framework's versatility and effectiveness by applying these
attribution methods to feature interactions in sentiment analysis and
chain-of-thought prompting.