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A Bayesian Nonparametric Approach to Causal Inference on Quantiles
We propose a Bayesian nonparametric approach (BNP) for causal inference on quantiles in the presence of many confounders. In particular, we define relevant causal quantities and specify BNP models to avoid bias from restrictive parametric assumptions. We first use Bayesian additive regression trees...
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| Publicado no: | Biometrics |
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| Main Authors: | , , |
| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
2018
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| Assuntos: | |
| Acesso em linha: | https://ncbi.nlm.nih.gov/pmc/articles/PMC7551426/ https://ncbi.nlm.nih.gov/pubmed/29478267 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1111/biom.12863 |
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