arXiv:2606.08196v1 Announce Type: cross Abstract: We study causal discovery from observational data when some variables are hidden and the data-generating process follows a location-scale noise model (LSNM). Existing methods that handle hidden confounders typically assume additive noise, but in practice, causes often modulate not just the mean but also the variance of their effects. We prove that acyclic directed mixed graphs (ADMGs) satisfying a bow-free condition are identifiable under LSNM with hidden variables, establishing the first identifiability result for causally insufficient models
This research addresses fundamental limitations in current causal discovery methods, specifically the assumption of additive noise that often does not hold in complex real-world systems with hidden variables.
Improved causal discovery is critical for building more robust, explainable, and trustworthy AI systems, moving beyond correlation to true understanding of underlying mechanisms.
The ability to identify causal relationships more accurately in the presence of hidden variables and non-additive effects enhances the potential for AI to model complex systems, from biological processes to economic markets.
- · AI researchers
- · Data scientists
- · Developers of autonomous AI agents
- · Industries relying on predictive modeling
- · Developers of less sophisticated AI models
- · Systems relying solely on correlational analysis
This research provides a theoretical foundation for developing new causal inference algorithms capable of handling more realistic data complexities.
Improved causal models could lead to more effective interventions in domains like medicine, social policy, and economic forecasting, where understanding 'why' is crucial.
The enhanced interpretability and reliability of AI systems, driven by better causal understanding, could accelerate the adoption and trust in advanced AI agents across sensitive applications.
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Read at arXiv cs.LG