Cargando…
Discovery of interconnected causal drivers of COVID-19 vaccination intentions in the US using a causal Bayesian network
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...
Autores principales: | Fung, Henry, Sgaier, Sema K., Huang, Vincent S. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10188432/ https://www.ncbi.nlm.nih.gov/pubmed/37193707 http://dx.doi.org/10.1038/s41598-023-33745-4 |
Ejemplares similares
-
Causal Datasheet for Datasets: An Evaluation Guide for Real-World Data Analysis and Data Collection Design Using Bayesian Networks
por: Butcher, Bradley, et al.
Publicado: (2021) -
CausalTrail: Testing hypothesis using causal Bayesian networks
por: Stöckel, Daniel, et al.
Publicado: (2015) -
Causal Inference Network of Genes Related with Bone Metastasis of Breast Cancer and Osteoblasts Using Causal Bayesian Networks
por: Park, Sung Bae, et al.
Publicado: (2018) -
Causal discovery for the microbiome
por: Corander, Jukka, et al.
Publicado: (2022) -
Applying causal discovery to single-cell analyses using CausalCell
por: Wen, Yujian, et al.
Publicado: (2023)