VAC2: Visual Analysis of Combined Causality in Event Sequences

Understand

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.