The Loss of a False Avoidance Decision
Abstract
Simultaneous best-and-worst subsets can wrongly avoid a useful design as well as adopt a poor one. A fixed-list opportunity-cost formulation helps make those consequences explicit. Evaluation should retain both error types and the candidate coverage, instead of reporting one generic correct-selection score that treats small and severe mistakes equally. This focused synthesis develops an empirical comparison that preserves the terminal action, observation process, and total verification cost. Its proposed tests are prospective; no new simulation run, model-training result, or intervention effect is reported.
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