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Using XGBoost and SHAP to explain citizens’ differences in policy support for reimposing COVID-19 measures in the Netherlands

Author

Listed:
  • Jose Ignacio Hernandez

    (Universidad San Sebastian
    Delft University of Technology)

  • Sander Cranenburgh

    (Delft University of Technology)

  • Marijn Bruin

    (National Institute of Public Health and the Environment
    Institute of Health Sciences, IQ Healthcare, Radboud University Medical Center)

  • Marijn Stok

    (National Institute of Public Health and the Environment
    Utrecht University)

  • Niek Mouter

    (Delft University of Technology
    Populytics, Research Agency)

Abstract

Several studies examined what drives citizens’ support for COVID-19 measures, but no works have addressed how the effects of these drivers are distributed at the individual level. Yet, if significant differences in support are present but not accounted for, policymakers’ interpretations could lead to misleading decisions. In this study, we use XGBoost, a supervised machine learning model, combined with SHAP (Shapley Additive eXplanations) to identify the factors associated with differences in policy support for COVID-19 measures and how such differences are distributed across different citizens and measures. We use secondary data from a Participatory Value Evaluation (PVE) experiment, in which 1,888 Dutch citizens answered which COVID-19 measures should be imposed under four risk scenarios. We identified considerable heterogeneity in citizens’ support for different COVID-19 measures regarding different age groups, the weight given to citizens’ opinions and the perceived risk of getting sick of COVID-19. Data analysis methods employed in previous studies do not reveal such heterogeneity of policy support. Policymakers can use our results to tailor measures further to increase support for specific citizens/measures.

Suggested Citation

  • Jose Ignacio Hernandez & Sander Cranenburgh & Marijn Bruin & Marijn Stok & Niek Mouter, 2025. "Using XGBoost and SHAP to explain citizens’ differences in policy support for reimposing COVID-19 measures in the Netherlands," Quality & Quantity: International Journal of Methodology, Springer, vol. 59(1), pages 381-409, February.
    • Handle: RePEc:spr:qualqt:v:59:y:2025:i:1:d:10.1007_s11135-024-01938-2
    DOI: 10.1007/s11135-024-01938-2
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    References listed on IDEAS

    as
    1. Ji, Shujuan & Wang, Xin & Lyu, Tao & Liu, Xiaojie & Wang, Yuanqing & Heinen, Eva & Sun, Zhenwei, 2022. "Understanding cycling distance according to the prediction of the XGBoost and the interpretation of SHAP: A non-linear and interaction effect analysis," Journal of Transport Geography, Elsevier, vol. 103(C).
    2. Mulderij, Lisanne S. & Hernández, José Ignacio & Mouter, Niek & Verkooijen, Kirsten T. & Wagemakers, Annemarie, 2021. "Citizen preferences regarding the public funding of projects promoting a healthy body weight among people with a low income," Social Science & Medicine, Elsevier, vol. 280(C).
    3. Mouter, Niek & Jara, Karen Trujillo & Hernandez, Jose Ignacio & Kroesen, Maarten & de Vries, Martijn & Geijsen, Tom & Kroese, Floor & Uiters, Ellen & de Bruin, Marijn, 2022. "Stepping into the shoes of the policy maker: Results of a Participatory Value Evaluation for the Dutch long term COVID-19 strategy," Social Science & Medicine, Elsevier, vol. 314(C).
    4. Niek Mouter & Jose Ignacio Hernandez & Anatol Valerian Itten, 2021. "Public participation in crisis policymaking. How 30,000 Dutch citizens advised their government on relaxing COVID-19 lockdown measures," PLOS ONE, Public Library of Science, vol. 16(5), pages 1-42, May.
    5. Sebastian Bach & Alexander Binder & Grégoire Montavon & Frederick Klauschen & Klaus-Robert Müller & Wojciech Samek, 2015. "On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-46, July.
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