Conformal Inverse Optimization for Adherence-aware Prescriptive Analytics

Abstract

Inverse optimization is increasingly used to estimate unknown parameters in an optimization model based on decision data. We show that such a point estimate alone is insufficient in a prescriptive setting where the estimated parameters are used to prescribe new decisions. The resulting decisions may be low-quality and misaligned with human intuition and thus are unlikely to be adopted. To tackle this challenge, we propose a novel decision recommendation pipeline, which seeks to learn an uncertainty set for the unknown parameters and then solve a robust optimization model to prescribe new decisions. We show that the suggested decisions can achieve bounded optimality gaps, as evaluated using both the ground-truth parameters and human perceptions. Our method demonstrates strong empirical performance compared to the standard inverse optimization pipeline. Finally, we perform a case study where we apply this new pipeline to provide delivery route recommendations in Toronto, Canada. Our approach achieves a significantly higher delivery path adherence rate than current industry practices without compromising service quality. Moreover, our method provides a better trade-off between absolute and perceived decision quality than baselines under various realistic scenarios, including cases with model mis-specification and data scarcity.

Keywords: inverse optimization, robust optimization, prescriptive analytics, human-AI collaboration