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In this short technical note we propose a baseline for decision-awarelearning for contextual linear optimization, which solves stochastic linearoptimization when cost coefficients can be predicted based on contextinformation. We propose a decision-aware version of predict-then-optimize. Wereweigh the prediction error by the decision regret incurred by an (unweighted)pilot estimator of costs to obtain a decision-aware predictor, then optimizewith cost predictions from the decision-aware predictor. This method can bemotivated as a finite-difference, iterate-independent approximation of thegradients of previously proposed end-to-end learning algorithms; it is alsoconsistent with previously suggested intuition for end-to-end learning. Thisbaseline is computationally easy to implement with readily available reweightedprediction oracles and linear optimization, and can be implemented with convexoptimization so long as the prediction error minimization is convex.Empirically, we demonstrate that this approach can lead to improvements over a"predict-then-optimize" framework for settings with misspecified models, and iscompetitive with other end-to-end approaches. Therefore, due to its simplicityand ease of use, we suggest it as a simple baseline for end-to-end anddecision-aware learning.

A Note on Task-Aware Loss via Reweighing Prediction Loss by Decision-Regret