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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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| Publicado no: | Stat Med |
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| Main Authors: | , , , |
| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
John Wiley and Sons Inc.
2016
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| Assuntos: | |
| Acesso em linha: | https://ncbi.nlm.nih.gov/pmc/articles/PMC5084780/ https://ncbi.nlm.nih.gov/pubmed/27139250 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1002/sim.6973 |
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