Understanding Indirect Causal Relationships in Node‐Link Graphs

• Published in: Computer graphics forum Vol. 36; no. 3; pp. 411 - 421
• Main Authors: Bae, Juhee, Helldin, Tove, Riveiro, Maria
• Format: Journal Article
• Language: English
• Published: Oxford Blackwell Publishing Ltd 01.06.2017
• Subjects: Categories and Subject Descriptors (according to ACM CCS); Data analysis; Graphs; H.5.2 [Information Interfaces and Presentation]: User Interfaces—Evaluation/methodology; INF301 Data Science; Multivariate analysis; Skövde Artificial Intelligence Lab (SAIL)
• ISSN: 0167-7055; 1467-8659; 1467-8659
• DOI: 10.1111/cgf.13198

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.