G-computation, propensity score-based methods, and targeted maximum likelihood estimator for causal inference with different covariates sets: a comparative simulation study
Abstract Controlling for confounding bias is crucial in causal inference. Distinct methods are currently employed to mitigate the effects of confounding bias. Each requires the introduction of a set of covariates, which remains difficult to choose, especially regarding the different methods. We cond...
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Autores principales: | Arthur Chatton, Florent Le Borgne, Clémence Leyrat, Florence Gillaizeau, Chloé Rousseau, Laetitia Barbin, David Laplaud, Maxime Léger, Bruno Giraudeau, Yohann Foucher |
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Formato: | article |
Lenguaje: | EN |
Publicado: |
Nature Portfolio
2020
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Materias: | |
Acceso en línea: | https://doaj.org/article/694ea9cc77664ee690089eec2f9159a9 |
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