Energy-Based Model for Accurate Estimation of Shapley Values in Feature Attribution
This work addresses a bottleneck in explainable AI for practitioners needing reliable feature attribution, though it is incremental as it builds on existing Shapley value estimation methods.
The paper tackles the challenge of accurately estimating Shapley values for feature attribution in explainable AI by proposing EmSHAP, which uses an energy-based model and GRU-coupled partition function estimation to improve accuracy and scalability, achieving higher accuracy and better scalability in four case studies compared to competitive methods.
Shapley value is a widely used tool in explainable artificial intelligence (XAI), as it provides a principled way to attribute contributions of input features to model outputs. However, estimation of Shapley value requires capturing conditional dependencies among all feature combinations, which poses significant challenges in complex data environments. In this article, EmSHAP (Energy-based model for Shapley value estimation), an accurate Shapley value estimation method, is proposed to estimate the expectation of Shapley contribution function under the arbitrary subset of features given the rest. By utilizing the ability of energy-based model (EBM) to model complex distributions, EmSHAP provides an effective solution for estimating the required conditional probabilities. To further improve estimation accuracy, a GRU (Gated Recurrent Unit)-coupled partition function estimation method is introduced. The GRU network captures long-term dependencies with a lightweight parameterization and maps input features into a latent space to mitigate the influence of feature ordering. Additionally, a dynamic masking mechanism is incorporated to further enhance the robustness and accuracy by progressively increasing the masking rate. Theoretical analysis on the error bound as well as application to four case studies verified the higher accuracy and better scalability of EmSHAP in contrast to competitive methods.