New Paper on Visual Causal Analysis for Event Sequence Data at IEEE VIS 2020

Jin et al. introduce SeqCausal, a visual analytics approach to causal analysis of event sequence data.

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-dimensional event variables are often connected to rather complex underlying event excitation mechanisms that are hard to infer from limited observations. Many existing automated causal analysis techniques suffer from poor explainability and fail to include an adequate amount of human knowledge.

In a new paper to be presented at IEEE VIS 2020 and published in the January 2021 issue of IEEE Transactions on Visualization and Computer Graphics (TVCG), the authors introduce a visual analytics method for recovering causalities in event sequence data. The paper, titled Visual Causality Analysis of Event Sequence Data, is a collaborative effort between Zhuochen Jin, Shunan Guo, Nan Chen, and Nan Cao from Tongji University; Daniel Weiskopf from University of Stuttgart; and UNC VACLab member David Gotz.

The approach outlined in the paper extends the Granger causality analysis algorithm on Hawkes processes to incorporate user feedback into causal model refinement. The visualization system includes an interactive causal analysis framework that supports bottom-up causal exploration, iterative causal verification and refinement, and causal comparison through a set of novel visualizations and interactions. The article reports two forms of evaluation: a quantitative evaluation of the model improvements resulting from the user-feedback mechanism, and a qualitative evaluation through case studies in different application domains to demonstrate the usefulness of the system.

This is the second of two VACLab-authored papers that will be presented at IEEE VIS 2020. In the other paper, which we announced a few weeks ago, VACLab teammates David Borland, Jonathan Zhang, Smiti Kaul, and David Gotz present an approach to mitigating selection bias during visual analysis via dynamic reweighting algorithms.


Zhuochen Jin, Shunan Guo, Nan Chen, Daniel Weiskopf, David Gotz, Nan Cao (2021). Visual Causality Analysis of Event Sequence Data. IEEE Transactions on Visualization and Computer Graphics (Volume 27, Issue 1).