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【應數系演講】111-12-02林聖軒教授

        國立東華大學應用數學系

            專           

    主講人:林聖軒教授

     國立陽明交通大學統計學研究所 副教授

                         數據科學與工程研究所 合聘副教授

      題:From linear structural equation modeling to generalized     

               multiple mediation formula.

      間:111122(星期五) 15:10-16:40

      點:理工一館A318教室  

                                                         

      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.

           ※※※                       ※※※se1111202

 

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