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The MIDAS Touch: Accurate and Scalable Missing-Data Imputation with Deep Learning

Published online by Cambridge University Press:  26 February 2021

Ranjit Lall Open the ORCID record for Ranjit Lall [Opens in a new window]  and
Thomas Robinson Open the ORCID record for Thomas Robinson [Opens in a new window]
Ranjit Lall*
Affiliation:
Department of International Relations, London School of Economics and Political Science, London, UK. Email: [email protected]
Thomas Robinson
Affiliation:
School of Government and International Affairs, Durham University, Durham, UK. Email: [email protected]
*
Corresponding author Ranjit Lall
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Abstract

Principled methods for analyzing missing values, based chiefly on multiple imputation, have become increasingly popular yet can struggle to handle the kinds of large and complex data that are also becoming common. We propose an accurate, fast, and scalable approach to multiple imputation, which we call MIDAS (Multiple Imputation with Denoising Autoencoders). MIDAS employs a class of unsupervised neural networks known as denoising autoencoders, which are designed to reduce dimensionality by corrupting and attempting to reconstruct a subset of data. We repurpose denoising autoencoders for multiple imputation by treating missing values as an additional portion of corrupted data and drawing imputations from a model trained to minimize the reconstruction error on the originally observed portion. Systematic tests on simulated as well as real social science data, together with an applied example involving a large-scale electoral survey, illustrate MIDAS’s accuracy and efficiency across a range of settings. We provide open-source software for implementing MIDAS.

Keywords

missing datamultiple imputationimputation methodsmachine learning
Type
Article
Information
Political Analysis , Volume 30 , Issue 2 , April 2022 , pp. 179 - 196
Copyright
© The Author(s) 2021. Published by Cambridge University Press on behalf of the Society for Political Methodology

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Footnotes

Edited by Jeff Gill

References

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Lall and Robinson Dataset

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https://doi.org/10.7910/DVN/UPL4TT
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Lall and Robinson supplementary material

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