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arXiv · 2610.20342

When Does Task-Aware Imputation Matter? An Analysis of Robustness Under Train-Test Mismatch

Maria V. Salvador·Ana F. Sequeira·Jaime S. Cardoso
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Abstract

Missing data is a pervasive challenge in machine learning, often degrading predictive performance and complicating model deployment. Most existing approaches treat imputation as a preprocessing step independent of the downstream task, a separation that becomes problematic under train-test mismatches in missingness mechanisms or rates. We introduce a unified framework that treats missing entries as variables in a task-conditioned optimization problem. We instantiate this framework in two settings: an amortized approach for scenarios with missing data at both training and test time, and a latent optimization approach for settings where only training data is incomplete. The evaluation is conducted on 98 tabular datasets, and robustness under train-test mismatches is assessed on 36 datasets. Results reveal that reconstruction error constitutes a poor proxy for predictive utility (Spearman rho approximately 0.16 to 0.32), and that task-aware imputation becomes critical in scenarios where classical methods fail to recover complete-data performance, achieving win rates of up to 80% in challenging train-test mismatch scenarios.

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Maria V. Salvador, Ana F. Sequeira, Jaime S. Cardoso. 2026-10-14. When Does Task-Aware Imputation Matter? An Analysis of Robustness Under Train-Test Mismatch. https://arxiv.org/abs/2610.20342

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