Variational Shapley Network: A Probabilistic Approach to Self-Explaining Shapley values with Uncertainty Quantification

• URL: http://arxiv.org/abs/2402.04211v1
• Date: Tue, 6 Feb 2024 18:09:05 GMT
• Title: Variational Shapley Network: A Probabilistic Approach to Self-Explaining Shapley values with Uncertainty Quantification
• Authors: Mert Ketenci, I\~nigo Urteaga, Victor Alfonso Rodriguez, No\'emie Elhadad, Adler Perotte
• Abstract summary: Shapley values have emerged as a foundational tool in machine learning (ML) for elucidating model decision-making processes. We introduce a novel, self-explaining method that simplifies the computation of Shapley values significantly, requiring only a single forward pass.
• Score: 2.6699011287124366
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Shapley values have emerged as a foundational tool in machine learning (ML) for elucidating model decision-making processes. Despite their widespread adoption and unique ability to satisfy essential explainability axioms, computational challenges persist in their estimation when ($i$) evaluating a model over all possible subset of input feature combinations, ($ii$) estimating model marginals, and ($iii$) addressing variability in explanations. We introduce a novel, self-explaining method that simplifies the computation of Shapley values significantly, requiring only a single forward pass. Recognizing the deterministic treatment of Shapley values as a limitation, we explore incorporating a probabilistic framework to capture the inherent uncertainty in explanations. Unlike alternatives, our technique does not rely directly on the observed data space to estimate marginals; instead, it uses adaptable baseline values derived from a latent, feature-specific embedding space, generated by a novel masked neural network architecture. Evaluations on simulated and real datasets underscore our technique's robust predictive and explanatory performance.

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