Conformal Prediction for Dyadic Regression Under Complex Missingness
Abstract
We develop a framework for conformal prediction in dyadic regression problems under complex missingness mechanisms. At the theoretical level, we develop general technical tools for establishing finite-sample validity of conformal prediction under distributional invariance conditions weaker than exchangeability. A key result handles the case where the sample itself is a random subset of the index set, a setting not covered by existing theory. In addition, we propose conformal prediction procedures for jointly exchangeable arrays, including full conformal, split conformal, a row-column approach exploiting similarities within rows and columns, and a selective conformal procedure achieving mask-conditional validity. For missing elements, we establish asymptotic validity of a weighted conformal procedure under a nonparametric graphon model for the missingness mechanism. We further establish conditional validity results for both continuous and discrete responses; to the best of our knowledge, this is the first formal proof of asymptotic conditional validity for weighted conformal prediction under a missing-not-at-random assumption. The proposed methods are illustrated on synthetic and real network data.
Related
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BibTeX
@misc{lunde2026conformal,
title = {{Conformal Prediction for Dyadic Regression Under Complex Missingness}},
author = {Robert Lunde and Minjie Yang and Elizaveta Levina and Ji Zhu},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2606.11136},
eprint = {2606.11136},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2606.11136},
}
RIS
TY - UNPB
TI - Conformal Prediction for Dyadic Regression Under Complex Missingness
AU - Robert Lunde
AU - Minjie Yang
AU - Elizaveta Levina
AU - Ji Zhu
PY - 2026
DA - 2026/09/16
PB - arXiv
DO - 10.48550/arxiv.2606.11136
UR - https://arxiv.org/abs/2606.11136
AB - We develop a framework for conformal prediction in dyadic regression problems under complex missingness mechanisms. At the theoretical level, we develop general technical tools for establishing finite-sample validity of conformal prediction under distributional invariance conditions weaker than exchangeability. A key result handles the case where the sample itself is a random subset of the index set, a setting not covered by existing theory. In addition, we propose conformal prediction procedures for jointly exchangeable arrays, including full conformal, split conformal, a row-column approach exploiting similarities within rows and columns, and a selective conformal procedure achieving mask-conditional validity. For missing elements, we establish asymptotic validity of a weighted conformal procedure under a nonparametric graphon model for the missingness mechanism. We further establish conditional validity results for both continuous and discrete responses; to the best of our knowledge, this is the first formal proof of asymptotic conditional validity for weighted conformal prediction under a missing-not-at-random assumption. The proposed methods are illustrated on synthetic and real network data.
ER -