We address the problem of learning Granger causality from asynchronous, interdependent, multi-type event sequences. In particular, we are interested in discovering instance-level causal structures in an unsupervised manner. Instance-level causality identifies causal relationships among individual events, providing more fine-grained information for decision-making. Existing work in the literature either requires strong assumptions, such as linearity in the intensity function, or heuristically defined model parameters that do not necessarily meet the requirements of Granger causality. We propose Instance-wise Self-Attentive Hawkes Processes (ISAHP), a novel deep learning framework that can directly infer the Granger causality at the event instance level. ISAHP is the first neural point process model that meets the requirements of Granger causality. It leverages the self-attention mechanism of the transformer to align with the principles of Granger causality. We empirically demonstrate that ISAHP is capable of discovering complex instance-level causal structures that cannot be handled by classical models. We also show that ISAHP achieves state-of-the-art performance in proxy tasks involving type-level causal discovery and instance-level event type prediction.

翻译：我们研究了从异步、相互依赖的多类型事件序列中学习格兰杰因果关系的问题。特别地，我们关注以无监督方式发现实例级别的因果结构。实例级因果性能够识别单个事件之间的因果关系，为决策提供更细粒度的信息。现有文献中的方法要么需要强假设（如强度函数的线性性），要么依赖启发式定义的模型参数，这些参数不一定满足格兰杰因果关系的要求。我们提出了实例级自注意力霍克斯过程（ISAHP），这是一种新型深度学习框架，可直接推断事件实例层面的格兰杰因果关系。ISAHP是首个满足格兰杰因果关系要求的神经点过程模型，它利用Transformer的自注意力机制来契合格兰杰因果原理。实验证明，ISAHP能够发现经典模型无法处理的复杂实例级因果结构。我们还表明，ISAHP在类型级因果发现和实例级事件类型预测等代理任务中达到了最先进性能。