Since the proposal of Granger causality, many researchers have followed the idea and developed extensions to the original algorithm. The classic Granger causality test aims to detect the existence of the static causal relationship. Notably, a fundamental assumption underlying most previous studies is the stationarity of causality, which requires the causality between variables to keep stable. However, this study argues that it is easy to break in real-world scenarios. Fortunately, our paper presents an essential observation: if we consider a sufficiently short window when discovering the rapidly changing causalities, they will keep approximately static and thus can be detected using the static way correctly. In light of this, we develop EffCause, bringing dynamics into classic Granger causality. Specifically, to efficiently examine the causalities on different sliding window lengths, we design two optimization schemes in EffCause and demonstrate the advantage of EffCause through extensive experiments on both simulated and real-world datasets. The results validate that EffCause achieves state-of-the-art accuracy in continuous causal discovery tasks while achieving faster computation. Case studies from cloud system failure analysis and traffic flow monitoring show that EffCause effectively helps us understand real-world time-series data and solve practical problems.

自从格兰杰因果关系提出以来，许多研究人员都遵循这个想法并开发了原始算法的扩展。经典的格兰杰因果检验旨在检测静态因果关系的存在。值得注意的是，大多数先前研究的基本假设是因果关系的平稳性，这要求变量之间的因果关系保持稳定。然而，这项研究认为，在现实场景中很容易被破坏。幸运的是，我们的论文提出了一个重要的观察结果：如果我们在发现快速变化的因果关系时考虑足够短的窗口，它们将保持近似静态，因此可以使用静态方式正确检测。有鉴于此，我们开发了 EffCause，将动态引入经典的格兰杰因果关系中。具体来说，为了有效地检查不同滑动窗口长度上的因果关系，我们在 EffCause 中设计了两种优化方案，并通过对模拟和真实数据集的大量实验证明了 EffCause 的优势。结果验证了 EffCause 在连续因果发现任务中实现了最先进的准确性，同时实现了更快的计算。云系统故障分析和流量监控的案例表明，EffCause有效帮助我们理解现实世界的时序数据并解决实际问题。