E. Wang

Causality-Based Conformal Imputation Correction with Non-Random Missing Labels

This work proposes a model-agnostic framework to assess the accuracy of imputed labels and to correct imputations with large bias based on conformal prediction, and leverage conformal prediction to construct a valid prediction set for units with unobserved labels, and revise imputations that fall outside this set.