Understanding the shared mathematical structure of value estimation methods enables designing more statistically efficient estimators—EASE reduces mean squared error by jointly optimizing sampling and surrogate functions rather than treating them separately.
This paper explains how to efficiently estimate Shapley values and similar attribution methods that explain AI model decisions. The authors show that different estimation approaches share a common mathematical structure, then use this insight to design a better estimator (EASE) that reduces computational error by optimizing both the sampling strategy and the surrogate function used.