  Identifying causality behind complex systems plays a significant role in
different domains, such as decision making, policy implementations, and
management recommendations. However, existing causality studies on temporal
event sequences data mainly focus on individual causal discovery, which is
incapable of exploiting combined causality. To fill the absence of combined
causes discovery on temporal event sequence data,eliminating and recruiting
principles are defined to balance the effectiveness and controllability on
cause combinations. We also leverage the Granger causality algorithm based on
the reactive point processes to describe impelling or inhibiting behavior
patterns among entities. In addition, we design an informative and aesthetic
visual metaphor of "electrocircuit" to encode aggregated causality for ensuring
our causality visualization is non-overlapping and non-intersecting. Diverse
sorting strategies and aggregation layout are also embedded into our
parallel-based, directed and weighted hypergraph for illustrating combined
causality. Our developed combined causality visual analysis system can help
users effectively explore combined causes as well as an individual cause. This
interactive system supports multi-level causality exploration with diverse
ordering strategies and a focus and context technique to help users obtain
different levels of information abstraction. The usefulness and effectiveness
of the system are further evaluated by conducting a pilot user study and two
case studies on event sequence data.
