Thibaut Vidal, Polytechnique Montréal

Abstract

Contextual stochastic optimization combines auxiliary information and machine learning to solve problems subject to uncertainty. While this integrated approach can improve performance, it leads to complex decision pipelines that lack transparency. Yet, practitioners need to understand and trust new solutions in order to replace an existing policy. To explain the solutions of contextual stochastic problems, we revisit the concept of counterfactual explanations introduced in the classification setting. We identify minimum changes in the features of the context that lead to a change in the optimal decisions. We formalize the explanation problem and develop mixed-integer linear models to find optimal explanations of decisions obtained through random forests and nearest-neighbor predictors. We apply our approach to selected operations research problems, such as inventory management and routing, and show the value of the explanations obtained.