论文标题
统一的信息传递和因果关系理论
A unified theory of information transfer and causal relation
论文作者
论文摘要
通过转移熵和信息流量测量的耦合随机动力学之间的信息传递被认为是系统因果关系关系的物理过程。尽管信息传输分析在科学和工程领域都有蓬勃发展的应用，但有关其基础的关键奥秘仍未解决。根本但困难的问题涉及信息转移和因果关系如何起源，依赖于什么，它们彼此之间的差异以及它们是由统一和一般数量创建的。这些问题从本质上决定了通过信息传输来确定因果关系测量的有效性。在这里，我们追求的是信息转移和因果关系的完整理论基础。除了有条件地持有的这些概念之间的众所周知的关系之外，我们证明了信息传递和因果关系普遍起源于特定信息协同和冗余现象，其特征是高阶互信息。更重要的是，我们的理论在分析上解释了信息传递和因果关系与彼此之间的因果关系的机制。此外，我们的理论自然定义了基于高维耦合事件的信息传递和因果关系的效果大小。这些结果可能会提供与桥梁珍珠的因果推断理论的信息，协同作用和因果关系的统一观点，这些论点在物理学中的计算机科学和信息传输分析中。
Information transfer between coupled stochastic dynamics, measured by transfer entropy and information flow, is suggested as a physical process underlying the causal relation of systems. While information transfer analysis has booming applications in both science and engineering fields, critical mysteries about its foundations remain unsolved. Fundamental yet difficult questions concern how information transfer and causal relation originate, what they depend on, how they differ from each other, and if they are created by a unified and general quantity. These questions essentially determine the validity of causal relation measurement via information transfer. Here we pursue to lay a complete theoretical basis of information transfer and causal relation. Beyond the well-known relations between these concepts that conditionally hold, we demonstrate that information transfer and causal relation universally originate from specific information synergy and redundancy phenomena characterized by high-order mutual information. More importantly, our theory analytically explains the mechanisms for information transfer and causal relation to originate, vanish, and differ from each other. Moreover, our theory naturally defines the effect sizes of information transfer and causal relation based on high-dimensional coupling events. These results may provide a unified view of information, synergy, and causal relation to bridge Pearl's causal inference theory in computer science and information transfer analysis in physics.
