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.

翻译：时间序列中的因果发现是机器学习社区中的一个基本问题，能够实现复杂场景下的因果推理与决策。近年来，研究者通过将神经网络与格兰杰因果性相结合，成功发现了因果关系，但由于高度冗余的网络设计和庞大的因果图，这些方法在处理高维数据时性能大幅下降。此外，观测数据中的缺失条目进一步阻碍了因果结构的学习。为克服这些限制，我们提出了CUTS+，它基于格兰杰因果性的因果发现方法CUTS，并通过引入一种名为“由粗到细发现”（C2FD）的技术以及利用基于消息传递的图神经网络（MPGNN），提升了可扩展性。在模拟、准真实和真实数据集上的对比实验表明，CUTS+显著改善了高维数据在不同类型不规则采样条件下的因果发现性能。