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This paper introduces MissARF, a generative machine learning method using adversarial random forests to efficiently handle missing data in biostatistical analyses. The approach matches the quality of state-of-the-art imputation techniques while offering faster runtime and zero additional cost for multiple imputation scenarios. It represents a practical advancement in data synthesis methods specifically tailored for statistical modeling workflows.
Missing value imputation with adversarial random forests - MissARF
arXiv:2507.15681v2 Announce Type: replace-cross Abstract: Handling missing values is a common challenge in biostatistical analyses, typically addressed by imputation methods. We propose a novel, fast, and easy-to-use imputation method called missing value imputation with adversarial random forests (MissARF), based on generative machine learning, that provides both single and multiple imputation. MissARF employs adversarial random forest (ARF) for density estimation and data synthesis. To impute a missing value of an observation, we condition on the non-missing values and sample from the estimated conditional distribution generated by ARF. Our experiments demonstrate that MissARF performs comparably to state-of-the-art single and multiple imputation methods in terms of imputation quality and fast runtime with no additional costs for multiple imputation.
Abstract:Handling missing values is a common challenge in biostatistical analyses, typically addressed by imputation methods. We propose a novel, fast, and easy-to-use imputation method called missing value imputation with adversarial random forests (MissARF), based on generative machine learning, that provides both single and multiple imputation. MissARF employs adversarial random forest (ARF) for density estimation and data synthesis. To impute a missing value of an observation, we condition on the non-missing values and sample from the estimated conditional distribution generated by ARF. Our experiments demonstrate that MissARF performs comparably to state-of-the-art single and multiple imputation methods in terms of imputation quality and fast runtime with no additional costs for multiple imputation.
Machine Learning (stat.ML); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
arXiv:2507.15681 [stat.ML]
(or arXiv:2507.15681v2 [stat.ML] for this version)
https://doi.org/10.48550/arXiv.2507.15681 Focus to learn more arXiv-issued DOI via DataCite
https://doi.org/10.1002/sim.70379 Focus to learn more DOI(s) linking to related resources
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