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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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| 出版年: | Stat Methods Med Res |
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| 主要な著者: | , , , , |
| フォーマット: | Artigo |
| 言語: | Inglês |
| 出版事項: |
SAGE Publications
2020
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| 主題: | |
| オンライン･アクセス: | https://ncbi.nlm.nih.gov/pmc/articles/PMC7534201/ https://ncbi.nlm.nih.gov/pubmed/32450775 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1177/0962280220921909 |
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