Explainable Machine Learning and Stochastic Optimization: From Context to Decision and Back Again

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 to be willing to replace an existing policy. To explain the solutions to 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.

About Thibaut Vidal

Thibaut Vidal is a professor at the Department of Mathematics and Industrial Engineering of Polytechnique Montréal, Canada. His main domains of expertise are related to combinatorial optimization, heuristic search, and interpretable machine learning, with applications to logistics and supply chain management, production management, resource allocation, and information processing.