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Bayesian Nonparametric Generative Models for Causal Inference with Missing at Random Covariates
We propose a general Bayesian nonparametric (BNP) approach to causal inference in the point treatment setting. The joint distribution of the observed data (outcome, treatment, and confounders) is modeled using an enriched Dirichlet process. The combination of the observed data model and causal assum...
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| Gepubliceerd in: | Biometrics |
|---|---|
| Hoofdauteurs: | , , , , , |
| Formaat: | Artigo |
| Taal: | Inglês |
| Gepubliceerd in: |
2018
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| Onderwerpen: | |
| Online toegang: | https://ncbi.nlm.nih.gov/pmc/articles/PMC7568223/ https://ncbi.nlm.nih.gov/pubmed/29579341 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1111/biom.12875 |
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