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

De Stavola, Bianca L., Daniel, Rhian ORCID:, Ploubidis, George B. and Micali, Nadia 2015. Mediation analysis with intermediate confounding: structural equation modeling viewed through the causal inference lens. American Journal of Epidemiology 181 (1) , pp. 64-80. 10.1093/aje/kwu239

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The study of mediation has a long tradition in the social sciences and a relatively more recent one in epidemiology. The first school is linked to path analysis and structural equation models (SEMs), while the second is related mostly to methods developed within the potential outcomes approach to causal inference. 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. Therefore, it seems pertinent to revisit SEMs with intermediate confounders, armed with the formal definitions and (parametric) identification assumptions from causal inference. Here we investigate: 1) how identification assumptions affect the specification of SEMs, 2) whether the more restrictive SEM assumptions can be relaxed, and 3) whether existing sensitivity analyses can be extended to this setting. Data from the Avon Longitudinal Study of Parents and Children (1990–2005) are used for illustration.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Medicine
Subjects: R Medicine > R Medicine (General)
Uncontrolled Keywords: Eating disorders; Estimation by combination; G-computation; Parametric identification; Path analysis; Sensitivity analysis
Publisher: Oxford University Press
ISSN: 0002-9262
Date of First Compliant Deposit: 14 November 2017
Date of Acceptance: 11 August 2014
Last Modified: 08 May 2023 02:29

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