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Population-level and individual-level explainers for propensity score matching in observational studies
PRECIS: The exclusion of unmatched observations in propensity score matching has implications for the generalizability of causal effects. Machine learning methods can help to identify how the study population differs from the unmatched subpopulation. BACKGROUND: There has been widespread use of prop...
Autores principales: | Ghosh, Debashis, Amini, Arya, Jones, Bernard L., Karam, Sana D. |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9630947/ https://www.ncbi.nlm.nih.gov/pubmed/36338745 http://dx.doi.org/10.3389/fonc.2022.958907 |
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