mlsanityData to 7 Oct 2026

Multiple Imputation: A Review of Practical and Theoretical Findings

arXiv 1801.04058 ↗First posted 12 Jan 2018stat.MEcited by 14

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Abstract (arXiv)

Multiple imputation is a straightforward method for handling missing data in a principled fashion. This paper presents an overview of multiple imputation, including important theoretical results and their practical implications for generating and using multiple imputations. A review of strategies for generating imputations follows, including recent developments in flexible joint modeling and sequential regression/chained equations/fully conditional specification approaches. Finally, we compare and contrast different methods for generating imputations on a range of criteria before identifying promising avenues for future research.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 Jan 2018.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 2; 2022: 3; 2023: 0; 2024: 2; 2025: 5; 2026: 2.

14 papers.

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  1. 2021Identifiable Generative Models for Missing Not at Random Data Imputationcited by 30
  2. 2025A Unified Framework for Inference with General Missingness Patterns and Machine Learning Imputationcited by 5
  3. 2025Imputation-Powered Inferencecited by 4
  4. 2022Leveraging variational autoencoders for multiple data imputationcited by 3
  5. 2024Evaluating tree-based imputation methods as an alternative to MICE PMM for drawing inference in empirical studiescited by 2
  6. 2024A Bias-Variance Decomposition for Ensembles over Multiple Synthetic Datasetscited by 2
  7. 2021Convolutional generative adversarial imputation networks for spatio-temporal missing data in storm surge simulationscited by 1
  8. 2022A method for comparing multiple imputation techniques: a case study on the U.S. National COVID Cohort Collaborativecited by 1
  9. 2025kNNSampler: Stochastic Imputations for Recovering Missing Value Distributionscited by 1
  10. 2022Provable Guarantees for Sparsity Recovery with Deterministic Missing Data Patternscited by 0
  11. 2025Causal Effect Estimation with TMLE: Handling Missing Data and Near-Violations of Positivitycited by 0
  12. 2025An Interdisciplinary and Cross-Task Review on Missing Data Imputationcited by 0
2 more
  1. 2026tBayes-MICE: A Bayesian Approach to Multiple Imputation for Time Series Datacited by 0
  2. 2026Distributionally Faithful Imputation via Positive Semi-Definite Kernel Density Estimationcited by 0