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Unified causality analysis based on the degrees of freedom
András Telcs1, Marcell T Kurbucz2, Antal Jakovác1,3
1Wigner Research Centre for Physics, Department of Computational Sciences, Institute for Particle and Nuclear Physics, HUN-REN , 29-33 Konkoly-Thege Miklós Street, H-1121 Budapest, Hungary.
Physical Review. E
|August 19, 2025
Summary
This study introduces a unified method to identify causal relationships in dynamic systems, revealing hidden drivers and improving model accuracy. The approach analyzes system degrees of freedom for comprehensive causal insights.
Area of Science:
- Complex Systems Modeling
- Causal Inference
- Dynamical Systems Theory
Background:
- Accurate modeling of temporally evolving systems relies on understanding dynamic equations.
- Identifying causal relationships and hidden drivers is crucial for robust system modeling.
- Existing methods often struggle to uncover unobserved confounders influencing observed dynamics.
Purpose of the Study:
- To present a unified method for identifying fundamental causal relationships between systems.
- To uncover hidden common causes influencing observed system dynamics.
- To enhance the understanding of causal influence and hidden confounders in both deterministic and stochastic systems.
Main Methods:
- Analysis of degrees of freedom within the system.
- Development of a unified framework applicable to deterministic and stochastic models.
- Validation through theoretical models and simulations.
Main Results:
- Successfully identified fundamental causal relationships between system pairs.
- Uncovered previously unobserved hidden common causes.
- Demonstrated a more comprehensive understanding of causal influence and confounding factors.
Conclusions:
- The unified method provides a robust approach to causal discovery in dynamic systems.
- The framework effectively identifies both direct causal links and hidden confounders.
- Validated methods show potential for broad applications in complex system analysis.
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