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Copula Structural Equation Models for Mediation Pathway Analysis

In: Statistical Dependence Modeling

Author

Listed:
  • Canyi Chen

    (University of Michigan, Department of Biostatistics)

  • Ritoban Kundu

    (University of Michigan, Department of Biostatistics)

  • Wei Hao

    (University of Michigan, Department of Biostatistics)

  • Peter X. K. Song

    (University of Michigan, Department of Biostatistics)

Abstract

Structural equation models (SEMs) are fundamental to causal mediation pathway discovery. However, traditional SEM approaches often rely on ad hoc model specifications when handling complex data structures such as mixed data types or non-normal data in which Gaussian assumptions for errors are rather restrictive. The invocation of copula dependence modeling methods to extend the classical linear SEMs mitigates several of key technical limitations, offering greater modeling flexibility to analyze non-Gaussian data. This paper presents a selective review of major developments in this area, highlighting recent advancements and their methodological implications.

Suggested Citation

  • Canyi Chen & Ritoban Kundu & Wei Hao & Peter X. K. Song, 2026. "Copula Structural Equation Models for Mediation Pathway Analysis," Springer Books, in: Thomas Nagler & Dorota Kurowicka & Roger Cooke & Harry Joe (ed.), Statistical Dependence Modeling, pages 189-204, Springer.
    • Handle: RePEc:spr:sprchp:978-3-032-14252-8_9
    DOI: 10.1007/978-3-032-14252-8_9
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