  Causal discovery in time-series is a fundamental problem in the machine
learning community, enabling causal reasoning and decision-making in complex
scenarios. Recently, researchers successfully discover causality by combining
neural networks with Granger causality, but their performances degrade largely
when encountering high-dimensional data because of the highly redundant network
design and huge causal graphs. Moreover, the missing entries in the
observations further hamper the causal structural learning. To overcome these
limitations, We propose CUTS+, which is built on the Granger-causality-based
causal discovery method CUTS and raises the scalability by introducing a
technique called Coarse-to-fine-discovery (C2FD) and leveraging a
message-passing-based graph neural network (MPGNN). Compared to previous
methods on simulated, quasi-real, and real datasets, we show that CUTS+ largely
improves the causal discovery performance on high-dimensional data with
different types of irregular sampling.
