An Interdisciplinary and Cross-Task Review on Missing Data Imputation

TL;DR

This comprehensive review synthesizes missing data imputation methods across disciplines, covering classical, modern, and deep learning approaches, and discusses integration with downstream tasks, challenges, and future directions.

Contribution

It provides an interdisciplinary, cross-task categorization of imputation techniques, connecting statistical foundations with machine learning advances, and highlights future research challenges.

Findings

Classical and deep learning imputation methods are systematically categorized.

Integration of imputation with downstream tasks like classification is analyzed.

Future directions include privacy-preserving methods and generalizable models.

Abstract

Missing data is a fundamental challenge in data science, significantly hindering analysis and decision-making across a wide range of disciplines, including healthcare, bioinformatics, social science, e-commerce, and industrial monitoring. Despite decades of research and numerous imputation methods, the literature remains fragmented across fields, creating a critical need for a comprehensive synthesis that connects statistical foundations with modern machine learning advances. This work systematically reviews core concepts-including missingness mechanisms, single versus multiple imputation, and different imputation goals-and examines problem characteristics across various domains. It provides a thorough categorization of imputation methods, spanning classical techniques (e.g., regression, the EM algorithm) to modern approaches like low-rank and high-rank matrix completion, deep learning models (autoencoders, GANs, diffusion models, graph neural networks), and large language models. Special attention is given to methods for complex data types, such as tensors, time series, streaming data, graph-structured data, categorical data, and multimodal data. Beyond methodology, we investigate the crucial integration of imputation with downstream tasks like classification, clustering, and anomaly detection, examining both sequential pipelines and joint optimization frameworks. The review also assesses theoretical guarantees, benchmarking resources, and evaluation metrics. Finally, we identify critical challenges and future directions, emphasizing model selection and hyperparameter optimization, the growing importance of privacy-preserving imputation via federated learning, and the pursuit of generalizable models that can adapt across domains and data types, thereby outlining a roadmap for future research.