Estimate-Then-Optimize versus Integrated-Estimation-Optimization versus Sample Average Approximation: A Stochastic Dominance Perspective

Understand

In data-driven stochastic optimization, model parameters of the underlying distribution need to be estimated from data in addition to the optimization task.

• Recent literature considers integrating the estimation and optimization processes by selecting model parameters that lead to the best empirical objective performance.
• This integrated approach, which we call integrated-estimation-optimization (IEO), can be readily shown to outperform simple estimate-then-optimize (ETO) when the model is misspecified.
• In this paper, we show that a reverse behavior appears when the model class is well-specified and there is sufficient data.