ShipleyDouma2021

Référence

Shipley, B., Douma, J.C. (2021) Testing piecewise structural equations models in the presence of latent variables and including correlated errors. Structural Equation Modeling: A Multidisciplinary Journal. (URL )

Résumé

ABSTRACT Path models, expressed as Directed Acyclic Graphs (DAGs), and the testing of such DAGs via a d-sep test, have become popular because they can incorporate complicated data structures that are difficult or impossible to accommodate in classical structural equation modeling. However, d-sep tests cannot accommodate DAGs that include unmeasured (latent) variables. We describe (i) how to convert a DAG with latent variables into an observationally equivalent graph without latents (a Mixed Acyclic Graph, MAG), (ii) how this MAG identifies which latents can/cannot be ignored without changing the causal meaning of the original DAG, and (iii) how to perform the MAG equivalent of a d-sep test.

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@ARTICLE { ShipleyDouma2021,
    AUTHOR = { Shipley, B. and Douma, J.C. },
    JOURNAL = { Structural Equation Modeling: A Multidisciplinary Journal },
    TITLE = { Testing piecewise structural equations models in the presence of latent variables and including correlated errors },
    YEAR = { 2021 },
    NUMBER = { 0 },
    PAGES = { 1-8 },
    VOLUME = { 0 },
    ABSTRACT = { ABSTRACT Path models, expressed as Directed Acyclic Graphs (DAGs), and the testing of such DAGs via a d-sep test, have become popular because they can incorporate complicated data structures that are difficult or impossible to accommodate in classical structural equation modeling. However, d-sep tests cannot accommodate DAGs that include unmeasured (latent) variables. We describe (i) how to convert a DAG with latent variables into an observationally equivalent graph without latents (a Mixed Acyclic Graph, MAG), (ii) how this MAG identifies which latents can/cannot be ignored without changing the causal meaning of the original DAG, and (iii) how to perform the MAG equivalent of a d-sep test. },
    DOI = { 10.1080/10705511.2020.1871355 },
    EPRINT = { https://doi.org/10.1080/10705511.2020.1871355 },
    PUBLISHER = { Routledge },
    URL = { https://doi.org/10.1080/10705511.2020.1871355 },
}

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