Survey Methodology
On the use of machine learning methods for the treatment of unit nonresponse in surveys

by Khaled Larbi, John Tsang, David Haziza and Mehdi DagdougNote 1

  • Release date: June 30, 2025
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Abstract

In recent years, there has been a significant interest in machine learning in national statistical offices. Thanks to their flexibility, these methods may prove useful at the nonresponse treatment stage. In this article, we conduct an empirical investigation in order to compare several machine learning procedures in terms of bias and efficiency. In addition to the classical machine learning procedures, we assess the performance of ensemble approaches that make use of different machine learning procedures to produce a set of weights adjusted for nonresponse.

Key Words:      Aggregation procedure; Efficiency; Nonresponse bias; Propensity score estimation.

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How to cite

Larbi, K., Tsang, J., Haziza, D. and Dagdoug, M. (2025). On the use of machine learning methods for the treatment of unit nonresponse in surveys. Survey Methodology, 51(1), 275-303. Paper available at http://www.statcan.gc.ca/pub/12-001-x/2025001/article/00003-eng.pdf.

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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, 2025

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

Catalogue No. 12-001-X

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