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Mediation analysis with intermediate confounding: structural equation modeling viewed through the causal inference lens.

De Stavola BL, Daniel RM, Ploubidis GB, Micali N - Am. J. Epidemiol. (2014)

Bottom Line: By giving model-free definitions of direct and indirect effects and clear assumptions for their identification, the latter school has formalized notions intuitively developed in the former and has greatly increased the flexibility of the models involved.However, through its predominant focus on nonparametric identification, the causal inference approach to effect decomposition via natural effects is limited to settings that exclude intermediate confounders.Such confounders are naturally dealt with (albeit with the caveats of informality and modeling inflexibility) in the SEM framework.

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Causal diagram for exposure X, mediator M, outcome Y, background confounder C, and intermediate confounder L.
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KWU239F1: Causal diagram for exposure X, mediator M, outcome Y, background confounder C, and intermediate confounder L.

Mentions: We will discuss settings involving an exposure X, an outcome Y, a mediator M, background confounders C of 1 or more of the relationships X-Y, M-Y, and X-M, and intermediate confounders L of the M-Y relationship (Figure 1). The aim is to separate the causal effect of X acting along pathways that include M from the causal effect of X acting along other pathways that do not involve M (the indirect and direct effects, respectively).Figure 1.


Mediation analysis with intermediate confounding: structural equation modeling viewed through the causal inference lens.

De Stavola BL, Daniel RM, Ploubidis GB, Micali N - Am. J. Epidemiol. (2014)

Causal diagram for exposure X, mediator M, outcome Y, background confounder C, and intermediate confounder L.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4383385&req=5

KWU239F1: Causal diagram for exposure X, mediator M, outcome Y, background confounder C, and intermediate confounder L.
Mentions: We will discuss settings involving an exposure X, an outcome Y, a mediator M, background confounders C of 1 or more of the relationships X-Y, M-Y, and X-M, and intermediate confounders L of the M-Y relationship (Figure 1). The aim is to separate the causal effect of X acting along pathways that include M from the causal effect of X acting along other pathways that do not involve M (the indirect and direct effects, respectively).Figure 1.

Bottom Line: By giving model-free definitions of direct and indirect effects and clear assumptions for their identification, the latter school has formalized notions intuitively developed in the former and has greatly increased the flexibility of the models involved.However, through its predominant focus on nonparametric identification, the causal inference approach to effect decomposition via natural effects is limited to settings that exclude intermediate confounders.Such confounders are naturally dealt with (albeit with the caveats of informality and modeling inflexibility) in the SEM framework.

View Article: PubMed Central - PubMed

Show MeSH
Related in: MedlinePlus