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

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

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 dis...

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Bibliographic Details
Main Authors: Xiang Li, Shanghong Xie, Peter McColgan, Sarah J. Tabrizi, Rachael I. Scahill, Donglin Zeng, Yuanjia Wang
Format: Article
Language:English
Published: Frontiers Media S.A. 2018-10-01
Series:Frontiers in Genetics
Subjects:
graphical models
network analysis
causal structure discovery
heterogeneity
regularization
Online Access:https://www.frontiersin.org/article/10.3389/fgene.2018.00430/full

Internet

https://www.frontiersin.org/article/10.3389/fgene.2018.00430/full

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