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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; (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. Green open access

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Abstract

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.

Type: Article
Title: Mediation analysis with intermediate confounding: structural equation modeling viewed through the causal inference lens.
Location: United States
Open access status: An open access version is available from UCL Discovery
DOI: 10.1093/aje/kwu239
Publisher version: http://dx.doi.org/10.1093/aje/kwu239
Language: English
Additional information: © The Author 2014. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
Keywords: G-computation, eating disorders, estimation by combination, parametric identification, path analysis, sensitivity analysis, Adolescent, Body Mass Index, Causality, Confounding Factors (Epidemiology), Epidemiologic Methods, Feeding and Eating Disorders, Female, Humans, Mathematical Concepts, Models, Theoretical
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Education
UCL > Provost and Vice Provost Offices > School of Education > UCL Institute of Education
UCL > Provost and Vice Provost Offices > School of Education > UCL Institute of Education > IOE - Social Research Institute
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL GOS Institute of Child Health
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL GOS Institute of Child Health > Population, Policy and Practice Dept
URI: https://discovery.ucl.ac.uk/id/eprint/1475081
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