Survey Methodology
Are deep learning models superior for missing data imputation in surveys? Evidence from an empirical comparison

by Zhenhua Wang, Olanrewaju Akande, Jason Poulos and Fan LiNote 1

  • Release date: December 15, 2022
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Abstract

Multiple imputation (MI) is a popular approach for dealing with missing data arising from non-response in sample surveys. Multiple imputation by chained equations (MICE) is one of the most widely used MI algorithms for multivariate data, but it lacks theoretical foundation and is computationally intensive. Recently, missing data imputation methods based on deep learning models have been developed with encouraging results in small studies. However, there has been limited research on evaluating their performance in realistic settings compared to MICE, particularly in big surveys. We conduct extensive simulation studies based on a subsample of the American Community Survey to compare the repeated sampling properties of four machine learning based MI methods: MICE with classification trees, MICE with random forests, generative adversarial imputation networks, and multiple imputation using denoising autoencoders. We find the deep learning imputation methods are superior to MICE in terms of computational time. However, with the default choice of hyperparameters in the common software packages, MICE with classification trees consistently outperforms, often by a large margin, the deep learning imputation methods in terms of bias, mean squared error, and coverage under a range of realistic settings.

Key Words: Deep learning; Hyperparameter selection; Missing data; Multiple imputation by chained equations; Simulation studies; Survey data.

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How to cite

Wang, Z., Akande, O., Poulos, J. and Li, F. (2022). Are deep learning models superior for missing data imputation in surveys? Evidence from an empirical comparison. Survey Methodology, Statistics Canada, Catalogue No. 12-001-X, Vol. 48, No. 2. Paper available at http://www.statcan.gc.ca/pub/12-001-x/2022002/article/00009-eng.htm.

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ISSN : 1492-0921

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Survey Methodology publishes articles dealing with various aspects of statistical development relevant to a statistical agency, such as design issues in the context of practical constraints, use of different data sources and collection techniques, total survey error, survey evaluation, research in survey methodology, time series analysis, seasonal adjustment, demographic studies, data integration, estimation and data analysis methods, and general survey systems development. The emphasis is placed on the development and evaluation of specific methodologies as applied to data collection or the data themselves. All papers will be refereed. However, the authors retain full responsibility for the contents of their papers and opinions expressed are not necessarily those of the Editorial Board or of Statistics Canada.

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Published by authority of the Minister responsible for Statistics Canada.

© His Majesty the King in Right of Canada as represented by the Minister of Industry, 2022

Use of this publication is governed by the Statistics Canada Open Licence Agreement.

Catalogue No. 12-001-X

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Table of contents

Abstract - Article main page
Section 1. Introduction
Section 2. Missing data imputation methods
Section 3. Simulation-based evaluation of imputation methods
Section 4. Evaluation based on ACS
Section 5. Evaluation based on “benchmark” datasets
Section 6. Conclusion
Acknowledgements
References

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