论文标题
通过瞬态动力学和扰动推断动态系统的因果网络
Inferring Causal Networks of Dynamical Systems through Transient Dynamics and Perturbation
论文作者
论文摘要
从时间序列测量中推断出因果关系是一个数学问题，通常有无限数量的潜在解决方案可以重现给定的数据。我们深入探索一种策略，通过扰动网络来消除可能的基本因果网络，在该网络中，在该网络中，这些策略是随机靶向或应用的。产生的瞬态动力学提供了推断因果关系所需的关键信息。显示两种方法可提供准确的因果重建：带扰动的Granger因果关系（GC），以及我们提出的扰动级联推理（PCI）。扰动的GC能够在低耦合强度制度下推断较小的网络。我们提出的PCI方法在推断小（2-5个节点）和大型（10-20个节点）网络的因果关系方面表现出始终如一的性能，并具有线性和非线性动力学。因此，将大量多样化的扰动/驱动器应用于网络的能力对于成功，准确地确定因果关系并在各种可行的网络之间进行歧义至关重要。
Inferring causal relations from time series measurements is an ill-posed mathematical problem, where typically an infinite number of potential solutions can reproduce the given data. We explore in depth a strategy to disambiguate between possible underlying causal networks by perturbing the network, where the actuations are either targeted or applied at random. The resulting transient dynamics provide the critical information necessary to infer causality. Two methods are shown to provide accurate causal reconstructions: Granger causality (GC) with perturbations, and our proposed perturbation cascade inference (PCI). Perturbed GC is capable of inferring smaller networks under low coupling strength regimes. Our proposed PCI method demonstrated consistently strong performance in inferring causal relations for small (2-5 node) and large (10-20 node) networks, with both linear and nonlinear dynamics. Thus the ability to apply a large and diverse set of perturbations/actuations to the network is critical for successfully and accurately determining causal relations and disambiguating between various viable networks.
