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
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2020
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Acceso en línea:https://doaj.org/article/694ea9cc77664ee690089eec2f9159a9
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