When Robustness Meets Conservativeness: Conformalized Uncertainty Calibration for Balanced Decision Making
This addresses the challenge for practitioners in high-stakes decision-making to balance robustness and conservativeness, offering a principled data-driven methodology.
The paper tackles the problem of selecting robustness levels in robust optimization, which is often done ad hoc, by proposing a framework that provides distribution-free, finite-sample guarantees on both miscoverage and regret, enabling decision-makers to calibrate robustness levels based on cost-risk preferences.
Robust optimization safeguards decisions against uncertainty by optimizing against worst-case scenarios, yet their effectiveness hinges on a prespecified robustness level that is often chosen ad hoc, leading to either insufficient protection or overly conservative and costly solutions. Recent approaches using conformal prediction construct data-driven uncertainty sets with finite-sample coverage guarantees, but they still fix coverage targets a priori and offer little guidance for selecting robustness levels. We propose a new framework that provides distribution-free, finite-sample guarantees on both miscoverage and regret for any family of robust predict-then-optimize policies. Our method constructs valid estimators that trace out the miscoverage-regret Pareto frontier, enabling decision-makers to reliably evaluate and calibrate robustness levels according to their cost-risk preferences. The framework is simple to implement, broadly applicable across classical optimization formulations, and achieves sharper finite-sample performance than existing approaches. These results offer the first principled data-driven methodology for guiding robustness selection and empower practitioners to balance robustness and conservativeness in high-stakes decision-making.