该研究提出统一方法，识别系统间因果关系及隐藏驱动因素。
Unified Causality Analysis Based on the Degrees of Freedom
- 基于系统自由度分析，统一处理确定与随机系统因果关系。
- 可发现未观测变量引发的隐藏共同原因，提升建模准确性。
- 适用于动态系统建模，适合关注因果推断的研究者。
时变系统通常由动态方程建模。准确建模的关键挑战在于理解子系统间的因果关系，以及识别未观测隐藏驱动对可观测动态的影响。本文提出一种统一方法，能够识别任意两系统间的根本因果关系，无论其为确定性或随机性。该方法特别能揭示超出可观测变量的隐藏共同原因。通过分析系统的自由度，本方法提供了对因果影响和隐藏混淆因子更全面的理解。该统一框架在理论模型和仿真中得到验证，展现出鲁棒性与广泛应用潜力。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。