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Identifying mediating variables with graphical models: an application to the study of causal pathways in people living with HIV

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  • Adrian Dobra
  • Katherine Buhikire
  • Joachim G. Voss

Abstract

We empirically demonstrate that graphical models can be a valuable tool in the identification of mediating variables in causal pathways. We make use of graphical models to elucidate the causal pathway through which the treatment influences the levels of fatigue and weakness in people living with HIV (PLHIV) based on a secondary analysis of a categorical dataset collected in a behavioral clinical trial: is weakness a mediator for the treatment and fatigue, or is fatigue a mediator for the treatment and weakness? Causal mediation analysis could not offer any definite answers to these questions.

Suggested Citation

  • Adrian Dobra & Katherine Buhikire & Joachim G. Voss, 2020. "Identifying mediating variables with graphical models: an application to the study of causal pathways in people living with HIV," Journal of Applied Statistics, Taylor & Francis Journals, vol. 47(7), pages 1298-1314, May.
    • Handle: RePEc:taf:japsta:v:47:y:2020:i:7:p:1298-1314
    DOI: 10.1080/02664763.2019.1669543
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    Cited by:

    1. Wei, Zheng & Wang, Li & Liao, Shu-Min & Kim, Daeyoung, 2023. "On the exploration of regression dependence structures in multidimensional contingency tables with ordinal response variables," Journal of Multivariate Analysis, Elsevier, vol. 196(C).

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