arXiv:2603.08311v2 Announce Type: replace-cross Abstract: We study identifiability in continuous-time linear stationary stochastic differential equations with a known causal structure. Unlike existing approaches, we relax the assumption of a known diffusion matrix, thereby respecting the model's intrinsic scale invariance. Therefore, rather than recovering drift coefficients themselves, we introduce edge-sign identifiability: for a given causal structure, we ask whether the sign of a given drift entry is uniquely determined across all observational covariance matrices induced by parametrisatio
This paper addresses a fundamental problem in causal inference that has become increasingly critical with the demand for interpretable and reliable AI systems, especially in dynamic environments.
Improving the identifiability of causal effects is crucial for developing robust AI that can reason about interventions and consequences, rather than mere correlations, which is essential for agentic systems.
The ability to determine the sign of causal links, even without full knowledge of system parameters, offers a novel approach to understanding complex stochastic systems and refining causal AI models.
- · AI researchers
- · Developers of causal AI
- · Fields relying on complex dynamic modeling
- · Systems heavily reliant on correlation-based inference
- · AI models lacking causal interpretability
The methodology could lead to more accurate and reliable causal discovery algorithms for AI.
Enhanced causal understanding could accelerate the development of more sophisticated and trustworthy AI agents capable of nuanced decision-making.
These foundational advances might enable AI to autonomously design and execute complex experiments with better predictability of outcomes.
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Read at arXiv cs.LG