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Understanding Indirect Causal Relationships in Node‐Link Graphs
Author(s) -
Bae Juhee,
Helldin Tove,
Riveiro Maria
Publication year - 2017
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/cgf.13198
Subject(s) - computer science , causal model , readability , causality (physics) , causal inference , variable (mathematics) , node (physics) , causal analysis , causal structure , link (geometry) , multivariate statistics , econometrics , data mining , theoretical computer science , mathematics , machine learning , statistics , mathematical analysis , computer network , physics , structural engineering , quantum mechanics , engineering , programming language
To 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.