Learning Granger Causality from Instance-wise Self-attentive Hawkes Processes

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), …

assumptions asynchronous causality cs.lg decision event events fine-grained information instance literature making processes relationships stat.ml type unsupervised wise work