Group-Conditional Conformal Prediction
Overview
Conformal prediction uses calibration data to build prediction sets or intervals with coverage guarantees, while group-conditional methods seek reliability for groups such as classes, languages, or missing-data patterns. Common approaches include Mondrian calibration with separate group thresholds, hierarchical or learned subgrouping, and local or online calibration. These methods are used in settings from image classification and clinical prediction to language-model deferral and forecasting, where pooled coverage can hide failures for particular groups. A central tension is that finer-grained guarantees can require more calibration data and produce larger prediction sets, while distribution shifts can undermine group coverage.
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