A flexible, interpretable framework for assessing sensitivity to unmeasured confounding

When estimating causal effects, unmeasured confounding and model misspecification are both potential sources of bias. We propose a method to simultaneously address both issues in the form of a semi‐parametric sensitivity analysis. In particular, our approach incorporates Bayesian Additive Regression...

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Detalles Bibliográficos
Autores principales: Dorie, Vincent, Harada, Masataka, Carnegie, Nicole Bohme, Hill, Jennifer
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2016
Materias:
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Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5084780/
https://www.ncbi.nlm.nih.gov/pubmed/27139250
http://dx.doi.org/10.1002/sim.6973

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5084780/
https://www.ncbi.nlm.nih.gov/pubmed/27139250
http://dx.doi.org/10.1002/sim.6973

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