ESENSC: A Polynomial-Time Axiomatic Alternative to SHAP

We propose ESENSC, a computationally efficient and theoretically grounded alternative to SHAP. ESENSC is a polynomial-time attribution rule with a deterministic, closed-form representation that satisfies the null-player property and admits an axiomatic characterization based on efficiency, a restricted form of differential marginality, and explicit computational constraints. Empirically, ESENSC closely replicates the attribution patterns of exact SHAP by preserving its essential structural properties, while maintaining a fraction of the cost. Across neural network and XGBoost models, it achieves lower deviation and higher rank agreement than widely used sampling-based SHAP approximations. While the computational cost of exact SHAP grows exponentially with the number of features, ESENSC scales linearly, enabling reliable feature attribution in regimes where exact SHAP is intractable. These results demonstrate that ESENSC achieves near-SHAP attribution accuracy while reducing computational complexity from exponential to linear in the number of features, providing a practical and theoretically rigorous alternative for feature attribution in high-dimensional applications.