  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.
