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Beyond the Mean: A Flexible Framework for Studying Causal Effects Using Linear Models
Graph-based causal models are a flexible tool for causal inference from observational data. In this paper, we develop a comprehensive framework to define, identify, and estimate a broad class of causal quantities in linearly parametrized graph-based models. The proposed method extends the literature...
| Autores principales: | , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Springer US
2021
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9433367/ https://www.ncbi.nlm.nih.gov/pubmed/34894340 http://dx.doi.org/10.1007/s11336-021-09811-z |