ArticleScientific reports2023
Discovery of interconnected causal drivers of COVID-19 vaccination intentions in the US using a causal Bayesian network.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Causal Discovery in Observational Medical Research: Scoping Review.JMIR medical informatics · 2026Article
- Causal discovery methods in psychological research: Foundations, algorithms, and a practical tutorial in R.Behavior research methods · 2026Review
- Social networks and vaccination: a perspective on the essential role of social networks to improve vaccination outcomes in public health.Frontiers in public health · 2026Article
- Testicular self-examination: The role of anticipated relief and anticipated regret.British journal of health psychology · 2025Article
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Authors and funding
3 authors.
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Abstract
Holistic interventions to overcome COVID-19 vaccine hesitancy require a system-level understanding of the interconnected causes and mechanisms that give rise to it. However, conventional correlative analyses do not easily provide such nuanced insights. We used an unsupervised, hypothesis-free causal discovery algorithm to learn the interconnected causal pathways to vaccine intention as a causal Bayesian network (BN), using data from a COVID-19 vaccine hesitancy survey in the US in early 2021. We identified social responsibility, vaccine safety and anticipated regret as prime candidates for interventions and revealed a complex network of variables that mediate their influences. Social responsibility's causal effect greatly exceeded that of other variables. The BN revealed that the causal impact of political affiliations was weak compared with more direct causal factors. This approach provides clearer targets for intervention than regression, suggesting it can be an effective way to explore multiple causal pathways of complex behavioural problems to inform interventions.
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