  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.
