A complete graphical criterion for the adjustment formula in mediation analysis
A complete graphical criterion for the adjustment formula in mediation analysis
Various assumptions have been used in the literature to identify natural direct and indirect effects in mediation analysis. These effects are of interest because they allow for effect decomposition of a total effect into a direct and indirect effect even in the presence of interactions or non-linear models. In this paper, we consider the relation and interpretation of various identification assumptions in terms of causal diagrams interpreted as a set of non-parametric structural equations. We show that for such causal diagrams, two sets of assumptions for identification that have been described in the literature are in fact equivalent in the sense that if either set of assumptions holds for all models inducing a particular causal diagram, then the other set of assumptions will also hold for all models inducing that diagram. We moreover build on prior work concerning a complete graphical identification criterion for covariate adjustment for total effects to provide a complete graphical criterion for using covariate adjustment to identify natural direct and indirect effects. Finally, we show that this criterion is equivalent to the two sets of independence assumptions used previously for mediation analysis.
adjustment, causal diagrams, confounding, covariate adjustment, mediation, natural direct, indirect effects
1-24
Shpitser, Ilya
4d295b9b-39e8-417f-b38d-fbb5d7df6992
VanderWeele, Tyler J.
7ba69431-209e-4b4b-919e-aa109daa569d
4 March 2011
Shpitser, Ilya
4d295b9b-39e8-417f-b38d-fbb5d7df6992
VanderWeele, Tyler J.
7ba69431-209e-4b4b-919e-aa109daa569d
Shpitser, Ilya and VanderWeele, Tyler J.
(2011)
A complete graphical criterion for the adjustment formula in mediation analysis.
International Journal of Biostatistics, 7 (1), .
(doi:10.2202/1557-4679.1297).
Abstract
Various assumptions have been used in the literature to identify natural direct and indirect effects in mediation analysis. These effects are of interest because they allow for effect decomposition of a total effect into a direct and indirect effect even in the presence of interactions or non-linear models. In this paper, we consider the relation and interpretation of various identification assumptions in terms of causal diagrams interpreted as a set of non-parametric structural equations. We show that for such causal diagrams, two sets of assumptions for identification that have been described in the literature are in fact equivalent in the sense that if either set of assumptions holds for all models inducing a particular causal diagram, then the other set of assumptions will also hold for all models inducing that diagram. We moreover build on prior work concerning a complete graphical identification criterion for covariate adjustment for total effects to provide a complete graphical criterion for using covariate adjustment to identify natural direct and indirect effects. Finally, we show that this criterion is equivalent to the two sets of independence assumptions used previously for mediation analysis.
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Published date: 4 March 2011
Keywords:
adjustment, causal diagrams, confounding, covariate adjustment, mediation, natural direct, indirect effects
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Statistics
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Local EPrints ID: 350585
URI: http://eprints.soton.ac.uk/id/eprint/350585
ISSN: 1557-4679
PURE UUID: 60e733ee-82e6-4f67-b751-14e922f28789
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Date deposited: 08 Apr 2013 11:01
Last modified: 14 Mar 2024 13:29
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Author:
Ilya Shpitser
Author:
Tyler J. VanderWeele
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