Estimating Average Treatment Effects Utilizing Fractional Imputation when Confounders are Subject to Missingness
The problem of missingness in observational data is ubiquitous. When the confounders are missing at random, multiple imputation is commonly used; however, the method requires congeniality conditions for valid inferences, which may not be satisfied when estimating average causal treatment effects. Al...
| Principais autores: | , |
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| Formato: | Artigo |
| Idioma: | English |
| Publicado em: |
De Gruyter
2020-12-01
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| coleção: | Journal of Causal Inference |
| Assuntos: | |
| Acesso em linha: | https://doi.org/10.1515/jci-2019-0024 |