Copula Structural Equation Models for Mediation Pathway Analysis

Canyi Chen, Ritoban Kundu, Wei Hao and Peter X. K. Song ()
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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

A chapter in Statistical Dependence Modeling, 2026, pp 189-204 from Springer

Abstract: 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.

Keywords: Directed acyclic graph; Generalized SEM; Non-Gaussian data; Composite null; Adaptive bootstrap test (search for similar items in EconPapers)
Date: 2026
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DOI: 10.1007/978-3-032-14252-8_9

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