Monte Carlo sampling-based estimators of optimality gaps for stochastic programs are known to be biased. We present a method for reducing the bias of the estimators produced by the Averaged Two-Replication Procedure (A2RP) via a probability metrics approach, which can be done in polynomial time in sample size. We present analytic results for the newsvendor problem, and discuss further theoretical and computational results.
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