Learning Granger causality from event sequences is a challenging but essential task across various applications. Most existing methods rely on the assumption that event sequences are independent and identically distributed (i.i.d.). However, this i.i.d. assumption is often violated due to the inherent dependencies among the event sequences. Fortunately, in practice, we find these dependencies can be modeled by a topological network, suggesting a potential solution to the non-i.i.d. problem by introducing the prior topological network into Granger causal discovery. This observation prompts us to tackle two ensuing challenges: 1) how to model the event sequences while incorporating both the prior topological network and the latent Granger causal structure, and 2) how to learn the Granger causal structure. To this end, we devise a unified topological neural Poisson auto-regressive model with two processes. In the generation process, we employ a variant of the neural Poisson process to model the event sequences, considering influences from both the topological network and the Granger causal structure. In the inference process, we formulate an amortized inference algorithm to infer the latent Granger causal structure. We encapsulate these two processes within a unified likelihood function, providing an end-to-end framework for this task. Experiments on simulated and real-world data demonstrate the effectiveness of our approach.

翻译：从事件序列中学习格兰杰因果关系是一项具有挑战性但在各类应用中至关重要的任务。现有方法大多依赖于事件序列独立同分布的假设。然而，由于事件序列之间固有的依赖关系，这种独立同分布假设常常被违反。幸运的是，在实践中我们发现这些依赖关系可以通过拓扑网络建模，这提示了一种通过将先验拓扑网络引入格兰杰因果发现来解决非独立同分布问题的潜在方案。这一观察促使我们应对以下两个挑战：1）如何在建模事件序列时同时整合先验拓扑网络和潜在格兰杰因果结构，2）如何学习格兰杰因果结构。为此，我们设计了一个统一的拓扑神经泊松自回归模型，包含两个过程。在生成过程中，我们采用神经泊松过程的变体对事件序列进行建模，同时考虑来自拓扑网络和格兰杰因果结构的影响。在推理过程中，我们制定了一个摊销推理算法来推断潜在的格兰杰因果结构。我们将这两个过程封装在一个统一的似然函数中，为该任务提供了一个端到端框架。在模拟数据和真实数据上的实验证明了我们方法的有效性。