  Data-driven approaches to predict-then-optimize decision-making problems seek
to mitigate the risk of uncertainty region misspecification in safety-critical
settings. Current approaches, however, suffer from considering overly
conservative uncertainty regions, often resulting in suboptimal decisionmaking.
To this end, we propose Conformal-Predict-Then-Optimize (CPO), a framework for
leveraging highly informative, nonconvex conformal prediction regions over
high-dimensional spaces based on conditional generative models, which have the
desired distribution-free coverage guarantees. Despite guaranteeing robustness,
such black-box optimization procedures alone inspire little confidence owing to
the lack of explanation of why a particular decision was found to be optimal.
We, therefore, augment CPO to additionally provide semantically meaningful
visual summaries of the uncertainty regions to give qualitative intuition for
the optimal decision. We highlight the CPO framework by demonstrating results
on a suite of simulation-based inference benchmark tasks and a vehicle routing
task based on probabilistic weather prediction.
