  We propose a new sparse Granger-causal learning framework for temporal event
data. We focus on a specific class of point processes called the Hawkes
process. We begin by pointing out that most of the existing sparse causal
learning algorithms for the Hawkes process suffer from a singularity in maximum
likelihood estimation. As a result, their sparse solutions can appear only as
numerical artifacts. In this paper, we propose a mathematically well-defined
sparse causal learning framework based on a cardinality-regularized Hawkes
process, which remedies the pathological issues of existing approaches. We
leverage the proposed algorithm for the task of instance-wise causal event
analysis, where sparsity plays a critical role. We validate the proposed
framework with two real use-cases, one from the power grid and the other from
the cloud data center management domain.
