Visual Causality Analysis of Event Sequence Data

@article{Jin2020VisualCA,
  title={Visual Causality Analysis of Event Sequence Data},
  author={Zhuochen Jin and Shunan Guo and Nan Chen and Daniel Weiskopf and David Gotz and Nan Cao},
  journal={IEEE Transactions on Visualization and Computer Graphics},
  year={2020},
  volume={27},
  pages={1343-1352}
}
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… 

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