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Most Downloaded Articles
- An Introduction to Causal Inference by Pearl, Judea
- Meta-Analysis of Observational Studies with Unmeasured Confounders by McCandless, Lawrence C.
- Accuracy of Conventional and Marginal Structural Cox Model Estimators: A Simulation Study by Xiao, Yongling/ Abrahamowicz, Michal and Moodie, Erica E. M.
- Evaluating treatment effectiveness in patient subgroups: a comparison of propensity score methods with an automated matching approach by Radice, Rosalba/ Ramsahai, Roland/ Grieve, Richard/ Kreif, Noemi/ Sadique, Zia and Sekhon, Jasjeet S.
- A Refreshing Account of Principal Stratification by Mealli, Fabrizia and Mattei, Alessandra
A Complete Graphical Criterion for the Adjustment Formula in Mediation Analysis
Citation Information: The International Journal of Biostatistics. Volume 7, Issue 1, Pages 1–24, ISSN (Online) 1557-4679, DOI: 10.2202/1557-4679.1297, March 2011
- Published Online:
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.