Complete effect decomposition for an arbitrary number of multiple ordered mediators with time-varying confounders: A method for generalized causal multi-mediation analysis

Author:

Tai An-Shun1ORCID,Lin Sheng-Hsuan2ORCID

Affiliation:

1. Department of Statistics, National Cheng Kung University, Tainan

2. Institute of Statistics, National Yang Ming Chiao Tung University, Hsinchu

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.

Funder

Ministry of Science and Technology, Taiwan

Publisher

SAGE Publications

Subject

Health Information Management,Statistics and Probability,Epidemiology

Reference63 articles.

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