ArticleQuality & quantity2025
Using XGBoost and SHAP to explain citizens' differences in policy support for reimposing COVID-19 measures in the Netherlands.
Article in Quality & quantity, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- A multiscale geospatial analysis integrating explainable machine learning to characterize spatial disparities in melanoma mortality associated with environmental justice and healthcare access in the contiguous United States.Frontiers in public health · 2026Article
- Non-linear impacts of local fiscal expenditure on farmers' income: A SHAP-informed machine-learning analysis.PloS one · 2026Article
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Authors and funding
5 authors.
Funding
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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. Supplementary Information: The online version contains supplementary material available at 10.1007/s11135-024-01938-2.
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