We introduce a new Shapley value approach for global sensitivity analysis and machine learning explainability. The method is based on the first-order partial derivatives of the underlying function. The computational complexity of the method is linear in dimension (number of features), as opposed to the exponential complexity of other Shapley value approaches in the literature. Examples from global sensitivity analysis and machine learning are used to compare the method numerically with activity scores, SHAP, and KernelSHAP.

翻译：我们提出了一种新的沙普利值方法，用于全局敏感性分析和机器学习可解释性。该方法基于目标函数的一阶偏导数，其计算复杂度与维度（特征数量）呈线性关系，而文献中其他沙普利值方法的复杂度呈指数级。通过全局敏感性分析和机器学习领域的实例，我们将该方法与活动得分、SHAP及KernelSHAP进行了数值比较。