The Weighted M\"obius Score: A Unified Framework for Feature Attribution

• URL: http://arxiv.org/abs/2305.09204v1
• Date: Tue, 16 May 2023 06:27:27 GMT
• Title: The Weighted M\"obius Score: A Unified Framework for Feature Attribution
• Authors: Yifan Jiang, Shane Steinert-Threlkeld
• Abstract summary: Feature attribution aims to explain the reasoning behind a black-box model's prediction by identifying the impact of each feature on the prediction. 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 shows that many different attribution methods for both individual features and feature interactions are special cases.
• Score: 17.358276581599643
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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