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Source note2 min read

RetiSEM: Generalising Causal Models for Fragmented Biomedical Data

Source

arXiv

arxiv.org/abs/2606.24488v1

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Plain-Language Brief

A new method called RetiSEM is proposed to learn causal models from fragmented biomedical data, where clinical, molecular, and imaging variables are often incomplete or not jointly observed. The method organizes variables into biologically informed blocks, applies forbidden-edge constraints, and decomposes pathway-level effects. It was evaluated on ten synthetic benchmarks and a real-world dataset, showing lower structural error and higher causal accuracy than unconstrained baselines.

What the Document Says

  • RetiSEM is a proposed domain-constrained structural equation modelling (SEM) framework for causal graph recovery and mediation analysis in fragmented biomedical data.
  • The variables are organized into biologically informed blocks.
  • Forbidden-edge constraints are applied.
  • Pathway-level effects are decomposed into TE, NDE, and NIE components.
  • The method was evaluated on ten synthetic benchmark scenarios varying in dimensionality, nonlinearity, causal depth, and pathway structure.
  • It was also tested on a real-world dataset combining NHANES clinical variables with externally derived retinal representations.
  • RetiSEM achieved lower structural error and higher causal accuracy than unconstrained baselines in the synthetic benchmarks.
  • In the real-data analysis, retinal variables behave mainly as downstream biomarker-like indicators with smaller but detectable indirect effects.
  • The findings support RetiSEM as an interpretable framework for testing structured causal hypotheses in limited-resource biomedical AI.

Key Concepts Mentioned

  • RetiSEM
  • Structural Equation Modeling (SEM)
  • Causal graph recovery
  • Mediation analysis
  • Fragmented biomedical data
  • Biologically informed blocks
  • Forbidden-edge constraints
  • Pathway-level effects
  • TE, NDE, and NIE components
  • Synthetic benchmark scenarios
  • Real-world dataset
  • NHANES clinical variables
  • Retinal representations

Claims Made by the Document

  • RetiSEM is a promising method for learning causal models from fragmented biomedical data.
  • The method achieves lower structural error and higher causal accuracy than unconstrained baselines in synthetic benchmarks.

Evidence Used

  • Evaluation on ten synthetic benchmark scenarios varying in dimensionality, nonlinearity, causal depth, and pathway structure.
  • Comparison with unconstrained baselines in the synthetic benchmarks.

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

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Limitations Noted by the Agent

Agent interpretation — not from the source.

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Open Questions

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Notes for Future Q&A

May include interpretation. Labeled where it does.

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Raw Abstract / Excerpt

Learning causal models from fragmented biomedical data is challenging because clinical, molecular, and imaging variables are often incomplete or not jointly observed. We propose RetiSEM, a domain-constrained structural equation modelling (SEM) framework for causal graph recovery and mediation analysis under limited multimodal resources. This proposed work organises variables into biologically informed blocks, applies forbidden-edge constraints, and decomposes pathway-level effects into TE, NDE, and NIE components. We evaluate RetiSEM across ten synthetic benchmark scenarios that vary in dimensionality, nonlinearity, causal depth, and pathway structure, together with a fragmented real-world setting that combines NHANES clinical variables with externally derived retinal representations. This approach achieves lower structural error and higher causal accuracy than unconstrained baselines across the synthetic benchmarks. In the real-data analysis, retinal variables behave mainly as downstream biomarker-like indicators, with smaller but detectable indirect effects. These findings support our strategy as an interpretable framework for testing structured causal hypotheses in limited-resource biomedical AI. The code and resources for this work are publicly available at: https://github.com/Inamullah-Colab/ReitSEM.