论文标题
在动态系统中可靠检测因果关系
Reliable Detection of Causal Asymmetries in Dynamical Systems
论文作者
论文摘要
关于因果影响的存在，力量和主导方向的知识对于理解复杂系统至关重要。但是，由于有限的现实数据，目前研究了动态系统不同可观察到的因果关系的方法。缺失是一种统计明确的方法，可以避免伪造阳性检测，同时对弱相互作用敏感。理想情况下，当发生同步时，它也应该能够推断出定向的因果影响。所提出的方法利用了流形的局部膨胀，以获取从两个可观察到的状态重建中信息丢失的上限估计值。它对没有因果影响的测试进行了测试。模拟数据表明，固有的噪声，与同步进行应对以及耐受性噪声是鲁棒的。
Knowledge about existence, strength, and dominant direction of causal influences is of paramount importance for understanding complex systems. With limited amounts of realistic data, however, current methods for investigating causal links among different observables from dynamical systems suffer from ambiguous results. Missing is a statistically well defined approach that avoids false positive detections while being sensitive for weak interactions. Ideally, it should be able to infer directed causal influences also when synchronizations occur. The proposed method exploits local inflations of manifolds to obtain estimates of upper bounds on the information loss among state reconstructions from two observables. It comes with a test for the absence of causal influences. Simulated data demonstrate that it is robust to intrinsic noise, copes with synchronizations, and tolerates also measurement noise.
