New Framework for Conformal Prediction in Dyadic Regression with Missing Data
Researchers have developed a theoretical framework for conformal prediction in dyadic regression settings where data is missing under complex, non-random mechanisms. The work generalizes existing conformal prediction theory beyond standard exchangeability assumptions, introducing several new procedures including a graphon-weighted approach for missing data. The results are significant because they provide the first formal proof of asymptotic conditional validity for weighted conformal prediction under a missing-not-at-random assumption.
A new preprint posted to arXiv introduces a comprehensive framework for applying conformal prediction—a method for constructing statistically valid prediction intervals—to dyadic regression problems, where observations represent relationships between pairs of entities such as network edges. The authors establish super-uniformity of conformal p-values under distributional invariance conditions weaker than classical exchangeability, and handle the technically challenging case where the observed sample is itself a random subset of the full index set using a novel bijection argument. Multiple conformal procedures are proposed for jointly exchangeable arrays, including full conformal, split conformal, a row-column approach, and a selective conformal method that achieves mask-conditional validity. For data missing under a nonparametric graphon model, the paper establishes asymptotic validity of a graphon-weighted conformal procedure. Notably, the authors claim this constitutes the first formal proof of asymptotic conditional validity for weighted conformal prediction under missing-not-at-random assumptions, a longstanding open problem in the field. The methods are validated on both synthetic datasets and real network data.
What's missing
As a preprint, this work has not yet undergone formal peer review. Computational scalability of the proposed procedures to very large networks is not fully characterized.
What different sources said
- arXiv stat.MLCenter
Conformal Prediction for Dyadic Regression Under Complex Missingness
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