Understanding Indirect Causal Relationships in Node-Link Graphs

dc.contributor.authorBae, Juheeen_US
dc.contributor.authorHelldin, Toveen_US
dc.contributor.authorRiveiro, Mariaen_US
dc.contributor.editorHeer, Jeffrey and Ropinski, Timo and van Wijk, Jarkeen_US
dc.date.accessioned2017-06-12T05:22:55Z
dc.date.available2017-06-12T05:22:55Z
dc.date.issued2017
dc.description.abstractTo find correlations and cause and effect relationships in multivariate data sets is central in many data analysis problems. A common way of representing causal relations among variables is to use node-link diagrams, where nodes depict variables and edges show relationships between them. When performing a causal analysis, analysts may be biased by the position of collected evidences, especially when they are at the top of a list. This is of crucial importance since finding a root cause or a derived effect, and searching for causal chains of inferences are essential analytic tasks when investigating causal relationships. In this paper, we examine whether sequential ordering influences understanding of indirect causal relationships and whether it improves readability of multi-attribute causal diagrams. Moreover, we see how people reason to identify a root cause or a derived effect. The results of our design study show that sequential ordering does not play a crucial role when analyzing causal relationships, but many connections from/to a variable and higher strength/certainty values may influence the process of finding a root cause and a derived effect.en_US
dc.description.number3
dc.description.sectionheadersMulti and High Dimensional Visualization
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume36
dc.identifier.doi10.1111/cgf.13198
dc.identifier.issn1467-8659
dc.identifier.pages411-421
dc.identifier.urihttps://doi.org/10.1111/cgf.13198
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf13198
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectH.5.2 [Information Interfaces and Presentation]
dc.subjectUser Interfaces
dc.subjectEvaluation/methodology
dc.titleUnderstanding Indirect Causal Relationships in Node-Link Graphsen_US
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