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.
CITATION STYLE
Bae, J., Helldin, T., & Riveiro, M. (2017). Identifying root cause and derived effects in causal relationships. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10273 LNCS, pp. 22–34). Springer Verlag. https://doi.org/10.1007/978-3-319-58521-5_2
Mendeley helps you to discover research relevant for your work.