專 題 演 講
講 題：From linear structural equation modeling to generalized
multiple mediation formula.
時 間：111年12月2日(星期五) 15:10-16:40
Causal mediation analysis is advantageous for mechanism investigation.
In settings with multiple causally ordered mediators, path-specific effects
(PSEs) have been introduced to specify the effects of certain
combinations of mediators. However, most PSEs are unidentifiable.
Interventional analogue of PSE (iPSE) 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 large number
of mediators. In this study, we provide a generalized definition of traditional
PSEs and iPSEs with a recursive formula, along with the required
assumptions for nonparametric identification. This work has three major
contributions: First, we developed a general approach (that includes notation,
definitions, and estimation methods) for causal mediation analysis with an
arbitrary number of multiple ordered mediators and with time-varying confounders.
Second, we demonstrate identified formula of iPSE is a general form of previous
mediation analysis. It is reduced to linear structural equation model under
linear or log-linear model, to causal mediation formula when only one mediator.
Third, a flexible algorithm built based on g-computation algorithm is proposed along
with a userfriendly software online. This approach is applied to a Taiwanese
cohort study for exploring the mechanism by which hepatitis C virus infection affects
mortality through hepatitis B virus infection, abnormal liver function, and hepatocellular
carcinoma. All methods and software proposed in this study contribute to comprehensively
decompose a causal effect confirmed by data science and help disentangling causal
mechanisms when multiple ordered mediators exist, which make the natural pathways complicated.
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