Variational Shapley Network: A Probabilistic Approach to Self-Explaining Shapley values with Uncertainty Quantification
<|reference_start|>Variational Shapley Network: A Probabilistic Approach to Self-Explaining Shapley values with Uncertainty Quantification: 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.<|reference_end|>
@article{ketenci2024variational,
    title={Variational Shapley Network: A Probabilistic Approach to Self-Explaining
  Shapley values with Uncertainty Quantification},
    author={Mert Ketenci, I~nigo Urteaga, Victor Alfonso Rodriguez, No'emie
  Elhadad, Adler Perotte},
    journal={arXiv preprint arXiv:2402.04211},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.04211},
    primaryClass={cs.LG stat.ML}
}