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

Jose Ignacio Hernandez (), Sander Cranenburgh (), Marijn Bruin (), Marijn Stok () and Niek Mouter ()
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Jose Ignacio Hernandez: Universidad San Sebastian
Sander Cranenburgh: Delft University of Technology
Marijn Bruin: National Institute of Public Health and the Environment
Marijn Stok: National Institute of Public Health and the Environment
Niek Mouter: Delft University of Technology

Quality & Quantity: International Journal of Methodology, 2025, vol. 59, issue 1, No 17, 409 pages

Abstract: 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.

Keywords: XGBoost; SHAP; Policy support; COVID-19; SARS-CoV-2; Participatory Value Evaluation (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s11135-024-01938-2

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