Speaker
Description
Temporally evolving systems are typically modeled by dynamic equations. A
key challenge in accurate modeling is understanding the causal relationships between subsystems, as well as identifying the presence and influence of unobserved hidden drivers on the observed dynamics. This paper presents a unified method capable of identifying fundamental causal relationships between pairs of systems, whether deterministic or stochastic. Notably, the method also uncovers hidden common causes among the observed variables. By analyzing the degrees of freedom in the system, our approach provides a more comprehensive understanding of both direct and indirect causal influences. This unified framework is validated through theoretical models and simulations, demonstratingits robustness and potential for broader application.
