First-Order Efficiency for Probabilistic Value Estimation via A Statistical Viewpoint
This work proposes an Efficiency-Aware Surrogate-adjusted Estimator (EASE) that directly chooses the sampling law and surrogate to minimize the leading mean squared error (MSE), and demonstrates that EASE consistently outperforms existing estimators for various probabilistic values.