Structural Hawkes Processes for Learning Causal Structure from Discrete-Time Event Sequences
This addresses a challenging task in causal inference for event sequence analysis, particularly in low-resolution data, with incremental improvements over existing Hawkes process methods.
The authors tackled the problem of learning causal structure from discrete-time event sequences, where existing methods fail due to the assumption of strict temporal precedence, and proposed Structural Hawkes Processes that leverage instantaneous effects, showing identifiability and verifying effectiveness on synthetic and real-world data.
Learning causal structure among event types from discrete-time event sequences is a particularly important but challenging task. Existing methods, such as the multivariate Hawkes processes based methods, mostly boil down to learning the so-called Granger causality which assumes that the cause event happens strictly prior to its effect event. Such an assumption is often untenable beyond applications, especially when dealing with discrete-time event sequences in low-resolution; and typical discrete Hawkes processes mainly suffer from identifiability issues raised by the instantaneous effect, i.e., the causal relationship that occurred simultaneously due to the low-resolution data will not be captured by Granger causality. In this work, we propose Structure Hawkes Processes (SHPs) that leverage the instantaneous effect for learning the causal structure among events type in discrete-time event sequence. The proposed method is featured with the minorization-maximization of the likelihood function and a sparse optimization scheme. Theoretical results show that the instantaneous effect is a blessing rather than a curse, and the causal structure is identifiable under the existence of the instantaneous effect. Experiments on synthetic and real-world data verify the effectiveness of the proposed method.