Explainable Data-Driven Optimization: From Context to Decision and Back Again
This addresses the problem of trust and understanding for practitioners in operations management, offering a novel approach to explainability in optimization, though it is incremental in applying existing interpretability concepts to a new context.
The paper tackles the lack of interpretability in data-driven optimization pipelines, which hinders adoption by practitioners, by introducing a counterfactual explanation methodology tailored to such problems, demonstrating it on inventory management and routing with specific methods for random forest and nearest-neighbor predictors.
Data-driven optimization uses contextual information and machine learning algorithms to find solutions to decision problems with uncertain parameters. While a vast body of work is dedicated to interpreting machine learning models in the classification setting, explaining decision pipelines involving learning algorithms remains unaddressed. This lack of interpretability can block the adoption of data-driven solutions as practitioners may not understand or trust the recommended decisions. We bridge this gap by introducing a counterfactual explanation methodology tailored to explain solutions to data-driven problems. We introduce two classes of explanations and develop methods to find nearest explanations of random forest and nearest-neighbor predictors. We demonstrate our approach by explaining key problems in operations management such as inventory management and routing.