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Understanding the relation of events plays an important role in differentdomains, such as identifying the reasons for users' certain actions fromapplication logs as well as explaining sports players' behaviors according tohistorical records. Co-occurrence has been widely used to characterize therelation of events. However, insights provided by the co-occurrence relationare vague, which leads to difficulties in addressing domain problems. In thispaper, we use causation to model the relation of events and present avisualization approach for conducting the causation analysis of eventsequences. We integrate automatic causal discovery methods into the approachand propose a model for detecting event causalities. Considering theinterpretability, we design a novel visualization named causal flow tointegrate the detected causality into timeline-based event sequencevisualizations. With this design, users can understand the occurrence ofcertain events and identify the causal pathways. We further implement aninteractive system to help users comprehensively analyze event sequences. Twocase studies are provided to evaluate the usability of the approach.

CausalFlow: Visual Analytics of Causality in Event Sequences