Learning Subject-Specific Directed Acyclic Graphs With Mixed Effects Structural Equation Models From Observational Data.

ABSTRACT: The identification of causal relationships between random variables from large-scale observational data using directed acyclic graphs (DAG) is highly challenging. We propose a new mixed-effects structural equation model (mSEM) framework to estimate subject-specific DAGs, where we represent joint distribution of random variables in the DAG as a set of structural causal equations with mixed effects. The directed edges between nodes depend on observed exogenous covariates on each of the individual and unobserved latent variables. The strength of the connection is decomposed into a fixed-effect term representing the average causal effect given the covariates and a random effect term representing the latent causal effect due to unobserved pathways. The advantage of such decomposition is to capt

SUBMITTER: Li X 

PROVIDER: S-EPMC6176748 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature