  Understanding the relation of events plays an important role in different
domains, such as identifying the reasons for users' certain actions from
application logs as well as explaining sports players' behaviors according to
historical records. Co-occurrence has been widely used to characterize the
relation of events. However, insights provided by the co-occurrence relation
are vague, which leads to difficulties in addressing domain problems. In this
paper, we use causation to model the relation of events and present a
visualization approach for conducting the causation analysis of event
sequences. We integrate automatic causal discovery methods into the approach
and propose a model for detecting event causalities. Considering the
interpretability, we design a novel visualization named causal flow to
integrate the detected causality into timeline-based event sequence
visualizations. With this design, users can understand the occurrence of
certain events and identify the causal pathways. We further implement an
interactive system to help users comprehensively analyze event sequences. Two
case studies are provided to evaluate the usability of the approach.
