Identifying Root Cause and Derived Effects in Causal Relationships

• Published in: Human Interface and the Management of Information: Information, Knowledge and Interaction Design Vol. 10273; pp. 22 - 34
• Main Authors: Bae, Juhee, Helldin, Tove, Riveiro, Maria
• Format: Book Chapter Conference Proceeding
• Language: English
• Published: Switzerland Springer International Publishing AG 2017; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Cause and effect; Graph visualization; INF301 Data Science; Skövde Artificial Intelligence Lab (SAIL); strenght and significance; Strength and significance; User study
• ISBN: 3319585207; 9783319585208; 3319585215; 9783319585215
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-319-58521-5_2

This paper focuses on identifying factors that influence the process of finding a root cause and a derived effect in causal node-link graphs with associated strength and significance depictions. We discuss in detail the factors that seem to be involved in identifying a global cause and effect based on the analysis of the results of an online user study with 44 participants, who used both sequential and non-sequential graph layouts. In summary, the results show that participants show geodesic-path tendencies when selecting causes and derived effects, and that context matters, i.e., participant’s own beliefs, experiences and knowledge might influence graph interpretation.