ShapFM: Amortized Shapley Estimation with a Prior-Fitted Model

Shapley values are among the most widely used tools for explainable AI, with applications ranging from feature attribution and data valuation to causal explanation. In these settings, a machine learning problem is represented as a cooperative game over $p$ players, and the goal is to estimate each player’s Shapley value. Traditional estimators query each game and estimate Shapley values from scratch. In contrast, recent amortized approaches such as FastSHAP and ExplainerPFN learn across related games to predict Shapley values directly. These methods exploit shared structure, but their predictions are static: once trained, they cannot improve as additional evaluations of the current game become available. We introduce ShapFM, an adaptive amortized estimator that combines the strengths of both approaches. ShapFM is a prior-fitted transformer-based model, pre-trained on synthetic ML games to estimate Shapley values given evaluations of a game. Once trained, it yields Shapley estimations for real-world ML games with one forward pass. Like a traditional estimator, its predictions improve as more game evaluations are provided; unlike a traditional estimator, it can leverage structure learned from many related games to obtain accurate estimates from very few queries. We design the architecture specifically around the structure of cooperative games, alternating attention over players and coalitions, incorporating the underlying inputs that define each game, and optionally using evaluations of related games as additional context. Across a broad collection of machine learning games, ShapFM is competitive with state-of-the-art, heavily optimized traditional estimators and can substantially outperform them in the low-query regime.