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

Bianca L De Stavola, Rhian M Daniel, George B Ploubidis

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    This study revisits structural equation models (SEMs) in epidemiology, integrating causal inference with intermediate confounders. It explores how identification assumptions impact SEM specification and sensitivity analyses for mediation effects.

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    G-computationeating disordersestimation by combinationparametric identificationpath analysissensitivity analysis

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    Area of Science:

    • Epidemiology
    • Social Sciences
    • Causal Inference

    Background:

    • Mediation analysis has distinct traditions in social sciences (path analysis, SEMs) and epidemiology (potential outcomes).
    • Causal inference offers model-free definitions of effects but struggles with intermediate confounders.
    • Structural equation models (SEMs) handle intermediate confounders but lack formal causal inference rigor.

    Purpose of the Study:

    • To integrate formal causal inference definitions into SEMs for mediation analysis with intermediate confounders.
    • To investigate the impact of identification assumptions on SEM specification.
    • To explore relaxing restrictive SEM assumptions and extending sensitivity analyses.

    Main Methods:

    • Revisiting SEMs incorporating formal definitions and parametric identification assumptions from causal inference.
    • Investigating the effects of identification assumptions on SEM specification.
    • Examining the potential to relax SEM assumptions and extend sensitivity analyses.

    Main Results:

    • The study provides a framework for SEMs that formally incorporates causal inference principles.
    • It addresses the challenge of intermediate confounders in mediation analysis.
    • The research explores the implications for model specification and sensitivity analysis.

    Conclusions:

    • Integrating causal inference with SEMs enhances mediation analysis, particularly with intermediate confounders.
    • This approach offers a more flexible and rigorous method for decomposing effects in epidemiological studies.
    • Further research can extend these methods for robust causal effect estimation.