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In data-driven optimization, the sample performance of the obtained decisiontypically incurs an optimistic bias against the true performance, a phenomenoncommonly known as the Optimizer's Curse and intimately related to overfittingin machine learning. Common techniques to correct this bias, such ascross-validation, require repeatedly solving additional optimization problemsand are therefore computationally expensive. We develop a general biascorrection approach, building on what we call Optimizer's Information Criterion(OIC), that directly approximates the first-order bias and does not requiresolving any additional optimization problems. Our OIC generalizes thecelebrated Akaike Information Criterion to evaluate the objective performancein data-driven optimization, which crucially involves not only model fittingbut also its interplay with the downstream optimization. As such it can be usedfor decision selection instead of only model selection. We apply our approachto a range of data-driven optimization formulations comprising empirical andparametric models, their regularized counterparts, and furthermore contextualoptimization. Finally, we provide numerical validation on the superiorperformance of our approach under synthetic and real-world datasets.

Optimizer's Information Criterion: Dissecting and Correcting Bias in Data-Driven Optimization