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  3. Bayesian Structured Mediation Analysis With Unobserved Confounders.
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  3. Bayesian Structured Mediation Analysis With Unobserved Confounders.

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Bayesian Structured Mediation analysis with Unobserved confounders.

Yuliang Xu1, Shu Yang2, Jian Kang3

  • 1Department of Statistics, University of Chicago, Chicago, IL 60637, United States.

Biometrics
|June 17, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

We developed a Bayesian method (BASMU) to reduce bias in causal mediation analysis, especially for brain imaging data. BASMU improves the accuracy of estimating direct and indirect effects, outperforming existing methods.

Keywords:
Bayesian non-parametricbrain imagemediation analysisspatial structureunobserved confounders

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Area of Science:

  • Neuroimaging
  • Biostatistics
  • Causal Inference

Background:

  • Causal mediation analysis is crucial for understanding complex relationships.
  • High-dimensional mediators, like brain imaging data, present challenges due to unobserved confounders.
  • Spatial structures in mediators can be influenced by unobserved subject-specific confounding.

Purpose of the Study:

  • To develop a robust method for causal mediation analysis with high-dimensional, spatially structured mediators.
  • To address the impact of unobserved confounders in mediation analysis.
  • To improve the accuracy of estimating Natural Indirect Effects (NIE) and Natural Direct Effects (NDE).

Main Methods:

  • Developed the BAyesian Structured Mediation analysis with Unobserved confounders (BASMU) framework.
  • Incorporated spatial latent subject-specific confounding effects into the outcome model.
  • Proposed a two-stage estimation algorithm for bias mitigation.
  • Established model identifiability conditions for BASMU.

    • Main Results:

    • Theoretical analysis confirmed reduced asymptotic bias in NIE and NDE estimation.
    • Extensive simulations demonstrated BASMU's substantial bias reduction across scenarios.
    • Application to fMRI data identified 2-4 times more significant mediation voxels compared to existing methods.
    • NIE increased by 41% and NDE decreased by 26% in the fMRI analysis.

    Conclusions:

    • BASMU effectively reduces bias caused by unobserved confounders in mediation analysis of structured, high-dimensional data.
    • The framework enhances the identification of significant mediation effects in neuroimaging studies.
    • BASMU offers a more accurate approach to estimating direct and indirect effects in complex biological systems.