Estimation of causal effects of multiple treatments in observational studies with a binary outcome

There is a dearth of robust methods to estimate the causal effects of multiple treatments when the outcome is binary. This paper uses two unique sets of simulations to propose and evaluate the use of Bayesian additive regression trees in such settings. First, we compare Bayesian additive regression...

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Detalles Bibliográficos
Autores principales: Hu, Liangyuan, Gu, Chenyang, Lopez, Michael, Ji, Jiayi, Wisnivesky, Juan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: SAGE Publications 2020
Materias:
Articles
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7534201/
https://www.ncbi.nlm.nih.gov/pubmed/32450775
http://dx.doi.org/10.1177/0962280220921909

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7534201/
https://www.ncbi.nlm.nih.gov/pubmed/32450775
http://dx.doi.org/10.1177/0962280220921909

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