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Complete effect decomposition for an arbitrary number of multiple ordered mediators with time-varying confounders: A method for generalized causal multi-mediation analysis.
- Source :
-
Statistical Methods in Medical Research . Jan2023, Vol. 32 Issue 1, p100-117. 18p. - Publication Year :
- 2023
-
Abstract
- Causal mediation analysis is advantageous for mechanism investigation. In settings with multiple causally ordered mediators, path-specific effects have been introduced to specify the effects of certain combinations of mediators. However, most path-specific effects are unidentifiable. An interventional analog of path-specific effects is adapted to address the non-identifiability problem. Moreover, previous studies only focused on cases with two or three mediators due to the complexity of the mediation formula in a large number of mediators. In this study, we provide a generalized definition of traditional path-specific effects and interventional path-specific effects with a recursive formula, along with the required assumptions for nonparametric identification. Subsequently, a general approach is developed with an arbitrary number of multiple ordered mediators and with time-varying confounders. All methods and software proposed in this study contribute to comprehensively decomposing a causal effect confirmed by data science and help disentangling causal mechanisms in the presence of complicated causal structures among multiple mediators. [ABSTRACT FROM AUTHOR]
- Subjects :
- *DATA science
*COUNTERFACTUALS (Logic)
Subjects
Details
- Language :
- English
- ISSN :
- 09622802
- Volume :
- 32
- Issue :
- 1
- Database :
- Academic Search Index
- Journal :
- Statistical Methods in Medical Research
- Publication Type :
- Academic Journal
- Accession number :
- 161130486
- Full Text :
- https://doi.org/10.1177/09622802221130580