First-Order Efficiency for Probabilistic Value Estimation via A Statistical Viewpoint

ArXi:2605.02827v1 Announce Type: new Probabilistic values, including Shapley values and semivalues, provide a model-agnostic framework to attribute the behavior of a black-box model to data points or features, with a wide range of applications including explainable artificial intelligence and data valuation. However, their exact computation requires utility evaluations over exponentially many coalitions, making Monte Carlo approximation essential in modern machine learning applications.