Learning the Causal Structure of Networked Dynamical Systems under Latent Nodes and Structured Noise
潜在节点与结构化噪声下网络动态系统的因果结构学习
This paper considers learning the hidden causal network of a linear networked dynamical system (NDS) from the time series data at some of its nodes -- partial observability. The dynamics of the NDS are driven by colored noise that generates spurious associations across pairs of nodes, rendering the problem much harder. To address the challenge of noise correlation and partial observability, we assign to each pair of nodes a feature vector computed from the time series data…