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In this paper, we propose a novel approach for data-driven decision-makingunder uncertainty in the presence of contextual information. Given a finitecollection of observations of the uncertain parameters and potentialexplanatory variables (i.e., the contextual information), our approach fits aparametric model to those data that is specifically tailored to maximizing thedecision value, while accounting for possible feasibility constraints. From amathematical point of view, our framework translates into a bilevel program,for which we provide both a fast regularization procedure and a big-M-basedreformulation that can be solved using off-the-shelf optimization solvers. Weshowcase the benefits of moving from the traditional scheme for modelestimation (based on statistical quality metrics) to decision-guided predictionusing three different practical problems. We also compare our approach withexisting ones in a realistic case study that considers a strategic powerproducer that participates in the Iberian electricity market. Finally, we usethese numerical simulations to analyze the conditions (in terms of the firm'scost structure and production capacity) under which our approach proves to bemore advantageous to the producer.

A bilevel framework for decision-making under uncertainty with contextual information