# Multiple Imputation: A Review of Practical and Theoretical Findings

arXiv: https://arxiv.org/abs/1801.04058 · HTML: https://mlsanity.com/paper/1801.04058/

## Key facts

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

## Built on by (most cited first, 14 of 14)

- [Identifiable Generative Models for Missing Not at Random Data Imputation](https://mlsanity.com/paper/2110.14708/) (2021)
- [A Unified Framework for Inference with General Missingness Patterns and Machine Learning Imputation](https://mlsanity.com/paper/2508.15162/) (2025)
- [Imputation-Powered Inference](https://mlsanity.com/paper/2509.13778/) (2025)
- [Leveraging variational autoencoders for multiple data imputation](https://mlsanity.com/paper/2209.15321/) (2022)
- [Evaluating tree-based imputation methods as an alternative to MICE PMM for drawing inference in empirical studies](https://mlsanity.com/paper/2401.09602/) (2024)
- [A Bias-Variance Decomposition for Ensembles over Multiple Synthetic Datasets](https://mlsanity.com/paper/2402.03985/) (2024)
- [Convolutional generative adversarial imputation networks for spatio-temporal missing data in storm surge simulations](https://mlsanity.com/paper/2111.02823/) (2021)
- [A method for comparing multiple imputation techniques: a case study on the U.S. National COVID Cohort Collaborative](https://mlsanity.com/paper/2206.06444/) (2022)
- [kNNSampler: Stochastic Imputations for Recovering Missing Value Distributions](https://mlsanity.com/paper/2509.08366/) (2025)
- [Provable Guarantees for Sparsity Recovery with Deterministic Missing Data Patterns](https://mlsanity.com/paper/2206.04893/) (2022)
- [Causal Effect Estimation with TMLE: Handling Missing Data and Near-Violations of Positivity](https://mlsanity.com/paper/2510.22202/) (2025)
- [An Interdisciplinary and Cross-Task Review on Missing Data Imputation](https://mlsanity.com/paper/2511.01196/) (2025)
- [tBayes-MICE: A Bayesian Approach to Multiple Imputation for Time Series Data](https://mlsanity.com/paper/2603.27142/) (2026)
- [Distributionally Faithful Imputation via Positive Semi-Definite Kernel Density Estimation](https://mlsanity.com/paper/2607.07767/) (2026)

## 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.