arXiv:2509.01916v2 Announce Type: replace Abstract: Causal disentanglement from soft interventions is identifiable under the assumptions of linear interventional faithfulness and availability of both observational and interventional data. Prior work has focused on unstructured observations without leveraging known relational context among measured entities. In many scientific applications, however, the measured variables come with an observed interaction network that provides structured context, such as protein-protein interactions and pathway-gene membership. We propose GraCE-VAE, a graph-awa
The increasing complexity and interconnectedness of AI models and data necessitate more robust causal representation learning techniques, especially as AI applications move towards real-world decision-making.
This research provides a foundational step towards more interpretable, reliable, and generalizable AI systems by enabling better understanding of causal relationships within complex network data.
The ability to integrate structured relational context (like interaction networks) into causal representation learning promises more accurate disentanglement of causal factors in AI models.
- · AI/ML Researchers
- · Data Scientists
- · Biomedical Research
- · Relational Database Vendors
- · Black-box AI models
- · Purely correlational data analysis
Improved performance and robustness of AI models in scientific and engineering domains.
Accelerated discovery of underlying causal mechanisms in complex systems, leading to new scientific breakthroughs.
Enhanced development of autonomous AI agents capable of understanding and manipulating causal structures in their environment.
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