  Causality is crucial to understanding the mechanisms behind complex systems
and making decisions that lead to intended outcomes. Event sequence data is
widely collected from many real-world processes, such as electronic health
records, web clickstreams, and financial transactions, which transmit a great
deal of information reflecting the causal relations among event types.
Unfortunately, recovering causalities from observational event sequences is
challenging, as the heterogeneous and high-dimensional event variables are
often connected to rather complex underlying event excitation mechanisms that
are hard to infer from limited observations. Many existing automated causal
analysis techniques suffer from poor explainability and fail to include an
adequate amount of human knowledge. In this paper, we introduce a visual
analytics method for recovering causalities in event sequence data. We extend
the Granger causality analysis algorithm on Hawkes processes to incorporate
user feedback into causal model refinement. The visualization system includes
an interactive causal analysis framework that supports bottom-up causal
exploration, iterative causal verification and refinement, and causal
comparison through a set of novel visualizations and interactions. We report
two forms of evaluation: a quantitative evaluation of the model improvements
resulting from the user-feedback mechanism, and a qualitative evaluation
through case studies in different application domains to demonstrate the
usefulness of the system.
