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Ten propositions on machine learning in official statistics

Author

Listed:
  • Arnout Delden

    (Statistics Netherlands)

  • Joep Burger

    (Statistics Netherlands)

  • Marco Puts

    (Statistics Netherlands)

Abstract

Machine learning (ML) is increasingly being used in official statistics with a range of different applications. The main focus of ML models is to accurately predict attributes of new, unlabeled cases whereas the focus of classical statistical models is to describe the relations between independent and dependent variables. There is already a lot of experience in the sound use of classical statistical models in official statistics, but for ML models this is still under development. Recent discussions concerning the quality aspects of using ML in official statistics have concentrated on its implications for existing quality frameworks. We are in favor of the use of ML in official statistics, but the main question remains as to what factors need to be considered when using ML models in official statistics. As a means of raising awareness regarding these factors, we pose ten propositions regarding the (sensible) use of ML in official statistics.

Suggested Citation

  • Arnout Delden & Joep Burger & Marco Puts, 2023. "Ten propositions on machine learning in official statistics," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 17(3), pages 195-221, December.
    • Handle: RePEc:spr:astaws:v:17:y:2023:i:3:d:10.1007_s11943-023-00330-0
    DOI: 10.1007/s11943-023-00330-0
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    References listed on IDEAS

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