Learning What Not to Impute: An Uncertainty-Aware Diffusion Framework for Meaningful Missingness

• URL: http://arxiv.org/abs/2606.05073v1
• Date: Wed, 03 Jun 2026 16:31:54 GMT
• Title: Learning What Not to Impute: An Uncertainty-Aware Diffusion Framework for Meaningful Missingness
• Abstract summary: We propose Diff-Joint, a diffusion-based framework for missing value imputation.<n>We show that Diff-Joint effectively identifies meaningfully missing entries while achieving competitive imputation accuracy and improved downstream task performance.
• Score: 21.05045478147808
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Missing value imputation is a fundamental task in machine learning, with most existing methods assuming that all missing entries correspond to unobserved regular values. In many real-world datasets, however, missingness may arise from two distinct sources: some entries are meaningfully missing (intrinsically absent and semantically valid), while others are missing due to the observation process and should be imputed. We formalize this distinction as a selective imputation problem, where the goal is to jointly infer which missing entries should be preserved and which should be recovered. To address this challenge, we propose Diff-Joint, a diffusion-based framework that jointly models tabular data together with a latent missingness mask. The method alternates between conditional sampling and uncertainty-aware aggregation to iteratively refine both imputed values and missingness labels. Empirical results on synthetic and real-world datasets demonstrate that Diff-Joint effectively identifies meaningfully missing entries while achieving competitive imputation accuracy and improved downstream task performance.

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