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Causal inference with observational data: the need for triangulation of evidence
The goal of much observational research is to identify risk factors that have a causal effect on health and social outcomes. However, observational data are subject to biases from confounding, selection and measurement, which can result in an underestimate or overestimate of the effect of interest....
| Autores principales: | , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
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Cambridge University Press
2021
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| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8020490/ https://www.ncbi.nlm.nih.gov/pubmed/33682654 http://dx.doi.org/10.1017/S0033291720005127 |