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Weighted Conformal Prediction Provides Adaptive and Valid Mask-Conditional Coverage for General Missing Data Mechanisms

arXiv 2512.14221 ↗First posted 16 Dec 2025stat.ML cs.LGcited by 0

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Abstract (arXiv)

Conformal prediction (CP) offers a principled framework for uncertainty quantification, but it fails to guarantee coverage when faced with missing covariates. In addressing the heterogeneity induced by various missing patterns, Mask-Conditional Valid (MCV) Coverage has emerged as a more desirable property than Marginal Coverage. In this work, we adapt split CP to handle missing values by proposing a preimpute-mask-then-correct framework that can offer valid coverage. We show that our method provides guaranteed Marginal Coverage and Mask-Conditional Validity for general missing data mechanisms. A key component of our approach is a reweighted conformal prediction procedure that corrects the prediction sets after distributional imputation (multiple imputation) of the calibration dataset, making our method compatible with standard imputation pipelines. We derive two algorithms, and we show that they are approximately marginally valid and MCV. We evaluate them on synthetic and real-world datasets. It reduces significantly the width of prediction intervals w.r.t standard MCV methods, while maintaining the target guarantees.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 28 of its 53 references that are in the map. First posted on arXiv 16 Dec 2025.

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References · 28

  1. 2021A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantificationcited by 792
  2. 2019Conformalized Quantile Regressioncited by 629
  3. 2019Conformal Prediction Under Covariate Shiftcited by 519
  4. 2022Conformal prediction beyond exchangeabilitycited by 355
  5. 2018GAIN: Missing Data Imputation using Generative Adversarial Netscited by 352
  6. 2019GP-VAE: Deep Probabilistic Time Series Imputationcited by 172
  7. 2022Adaptive Conformal Predictions for Time Seriescited by 161
  8. 2018Handling Incomplete Heterogeneous Data using VAEscited by 149
  9. 2022Conformal Inference for Online Prediction with Arbitrary Distribution Shiftscited by 128
  10. 2021Distribution-free uncertainty quantification for classification under label shiftcited by 101
  11. 2022HyperImpute: Generalized Iterative Imputation with Automatic Model Selectioncited by 83
  12. 2020VAEM: a Deep Generative Model for Heterogeneous Mixed Type Datacited by 66
16 more
  1. 2021What's a good imputation to predict with missing values?cited by 62
  2. 2023ReMasker: Imputing Tabular Data with Masked Autoencodingcited by 37
  3. 2024Conformal Validity Guarantees Exist for Any Data Distribution (and How to Find Them)cited by 26
  4. 2023Conformal Prediction with Missing Valuescited by 25
  5. 2023Adaptive conformal classification with noisy labelscited by 25
  6. 2024Conformal Prediction: A Data Perspectivecited by 25
  7. 2023PAC Prediction Sets Under Label Shiftcited by 21
  8. 2022Label Noise Robustness of Conformal Predictioncited by 20
  9. 2023Bagging Provides Assumption-free Stabilitycited by 19
  10. 2022Domain Adaptation under Missingness Shiftcited by 13
  11. 2023Conformalized matrix completioncited by 13
  12. 2022Conformal Frequency Estimation using Discrete Sketched Data with Coverage for Distinct Queriescited by 10
  13. 2024Robust Conformal Prediction Using Privileged Informationcited by 7
  14. 2023Distribution-Free Matrix Prediction Under Arbitrary Missing Patterncited by 5
  15. 2024Structured Conformal Inference for Matrix Completion with Applications to Group Recommender Systemscited by 5
  16. 2024Predictive Uncertainty Quantification with Missing Covariatescited by 4

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