Conformal Repair on a Budget: An Empirical Protocol for Retraining Decisions

Distribution shift can compromise the empirical reliability of conformal prediction intervals, but a coverage failure does not by itself imply that the predictive model must be refit. This paper studies budgeted conformal repair, an empirical maintenance rule for finite samples that selects the first sufficient action under a prespecified intervention-cost ordering, subject to empirical coverage and interval-width constraints. Given an existing linear predictor, conformal intervals, labelled shifted repair data, a target coverage level, and a width budget, the rule compares no intervention, residual-scale recalibration, conformal recalibration, intercept-only updating, and a linear refit on update data. In a controlled Gaussian linear-regression testbed, the resulting minimal repair map separates no-failure cases, failures repaired by low-cost recalibration or bias updating, failures for which a full linear refit is the minimal sufficient action, and failures that remain unrepairable within the width budget. Additional mixed-shift experiments show that compounding residual inflation with bias or slope shift can move otherwise repairable failures into the unrepairable-within-budget category. Many reliability failures are repairable without refitting the predictor. High noise-scale shifts are often width-limited rather than refit-limited, while large pure slope shifts provide the clearest case where refitting becomes necessary. The contribution is a concrete empirical decision rule for distinguishing lower-intervention repair, refitting on update data, and no acceptable repair under a stated width budget.