Explainable Data-Driven Optimization: From Context to Decision and Back Again

Understand

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.