Risk Guarantees for End-to-End Prediction and Optimization Processes

Nam Ho-Nguyen () and Fatma Kılınç-Karzan ()
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Nam Ho-Nguyen: Discipline of Business Analytics, The University of Sydney, Sydney, New South Wales 2006, Australia
Fatma Kılınç-Karzan: Tepper School of Business, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213

Management Science, 2022, vol. 68, issue 12, 8680-8698

Abstract: Prediction methods are often employed to estimate parameters of optimization models. Although the goal in an end-to-end framework is to achieve good performance on the subsequent optimization model, a formal understanding of the ways in which prediction methods can affect optimization performance is notably lacking. This paper identifies conditions on prediction methods that can guarantee good optimization performance. We provide two types of results: asymptotic guarantees under a well-known Fisher consistency criterion and nonasymptotic performance bounds under a more stringent criterion. We use these results to analyze optimization performance for several existing prediction methods and show that in certain settings, methods tailored to the optimization problem can fail to guarantee good performance. Conversely, optimization-agnostic methods can sometimes, surprisingly, have good guarantees. In a computational study on portfolio optimization, fractional knapsack, and multiclass classification problems, we compare the optimization performance of several prediction methods. We demonstrate that lack of Fisher consistency of the prediction method can indeed have a detrimental effect on performance.

Keywords: stochastic optimization; prediction; end-to-end (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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