Unified Causality Analysis Based on the Degrees of Freedom
This work addresses the problem of accurate causal modeling in dynamic systems for researchers and practitioners, though it appears incremental as it builds on existing degrees of freedom analysis.
The paper tackles the challenge of modeling causal relationships and hidden drivers in temporally evolving systems by introducing a unified method that identifies causal relationships between pairs of systems and uncovers hidden common causes, validated through theoretical models and simulations.
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 beyond the observed variables. By analyzing the degrees of freedom in the system, our approach provides a more comprehensive understanding of both causal influence and hidden confounders. This unified framework is validated through theoretical models and simulations, demonstrating its robustness and potential for broader application.