Unified Causality Analysis Based on the Degrees of Freedom

TL;DR

This paper introduces a unified causality analysis method based on degrees of freedom that identifies direct causal links and hidden confounders in dynamic systems, applicable to both deterministic and stochastic models.

Contribution

The paper presents a novel framework that unifies causal inference and hidden cause detection using degrees of freedom analysis, applicable to various types of dynamic systems.

Findings

Successfully identifies causal relationships in theoretical models

Detects hidden common causes beyond observed variables

Demonstrates robustness through simulations

Abstract

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.