TY - JOUR
T1 - Complete effect decomposition for an arbitrary number of multiple ordered mediators with time-varying confounders
T2 - A method for generalized causal multi-mediation analysis
AU - Tai, An Shun
AU - Lin, Sheng Hsuan
N1 - Funding Information:
The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Ministry of Science and Technology, Taiwan (grant no. 108-2636-B-009-001).
Funding Information:
We thank Professor Hwai-I Yang (Genomics Research Center, Academia Sinica, Taipei, Taiwan) for providing the HCC data set in this article. We also thank Shih-Wen Lin and Ying-Wen Liang for their valuable comments on writing. This manuscript was edited by Wallace Academic Editing. The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Ministry of Science and Technology, Taiwan (grant no. 108-2636-B-009-001).
Publisher Copyright:
© The Author(s) 2022.
PY - 2023/1
Y1 - 2023/1
N2 - 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.
AB - 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.
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U2 - 10.1177/09622802221130580
DO - 10.1177/09622802221130580
M3 - Article
C2 - 36321187
AN - SCOPUS:85141396097
SN - 0962-2802
VL - 32
SP - 100
EP - 117
JO - Statistical Methods in Medical Research
JF - Statistical Methods in Medical Research
IS - 1
ER -