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Robust and Efficient Semi-Supervised Estimation of Average Treatment Effects with Application to Electronic Health Records Data
We consider the problem of estimating the average treatment effect (ATE) in a semi-supervised learning setting, where a very small proportion of the entire set of observations are labeled with the true outcome but features predictive of the outcome are available among all observations. This problem...
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| Publicado no: | Biometrics |
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| Main Authors: | , , |
| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
2020
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| Assuntos: | |
| Acesso em linha: | https://ncbi.nlm.nih.gov/pmc/articles/PMC7758040/ https://ncbi.nlm.nih.gov/pubmed/32413171 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1111/biom.13298 |
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