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• W4385422838 abstract "Traditional mediation analysis, which relies on linear regression models, has faced criticism due to its limited suitability for cases involving different types of variables and complex covariates, such as interactions. This can result in unclear definitions of direct and indirect effects. As an alternative, causal mediation analysis using the counterfactual framework has been introduced to provide clearer definitions of direct and indirect effects while allowing for more flexible modeling methods. However, the conceptual understanding of this approach based on the counterfactual framework remains challenging for applied researchers. To address this issue, the present article was written to highlight and illustrate the definitions of causal estimands, including controlled direct effect, natural direct effect, and natural indirect effect, based on the key concept of nested counterfactuals. Furthermore, we recommend using 2 R packages, ‘medflex’ and ‘mediation’, to perform causal mediation analysis and provide public health examples. The article also offers caveats and guidelines for accurate interpretation of the results." @default.
• W4385422838 created "2023-08-01" @default.
• W4385422838 creator A5030458503 @default.
• W4385422838 creator A5035829875 @default.
• W4385422838 date "2023-07-31" @default.
• W4385422838 modified "2023-10-16" @default.
• W4385422838 title "An Introduction to Causal Mediation Analysis With a Comparison of 2 R Packages" @default.
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• W4385422838 doi "https://doi.org/10.3961/jpmph.23.189" @default.
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