Visual Causality: Investigating Graph Layouts for Understanding Causal Processes

Vo, D.-B. , Lazarova, K., Purchase, H. C. and McCann, M. (2020) Visual Causality: Investigating Graph Layouts for Understanding Causal Processes. In: 11th International Conference on the Theory and Application of Diagrams (Diagrams 2020), Tallinn, Estonia, 24-28 Aug 2020, pp. 332-347. ISBN 9783030542481 (doi: 10.1007/978-3-030-54249-8_26)

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Causal diagrams provide a graphical formalism indicating how statistical models can be used to study causal processes. Despite the extensive research on the efficacy of aesthetic graphic layouts, the causal inference domain has not benefited from the results of this research. In this paper, we investigate the performance of graph visualisations for supporting users’ understanding of causal graphs. Two studies were conducted to compare graph visualisations for understanding causation and identifying confounding variables in a causal graph. The first study results suggest that while adjacency matrix layouts are better for understanding direct causation, node-link diagrams are better for understanding mediated causation along causal paths. The second study revealed that node-link layouts, and in particular layouts created by a radial algorithm, are more effective for identifying confounder and collider variables.

Item Type:Conference Proceedings
Glasgow Author(s) Enlighten ID:Vo, Dr Dong-Bach and Purchase, Dr Helen and McCann, Dr Mark
Authors: Vo, D.-B., Lazarova, K., Purchase, H. C., and McCann, M.
College/School:College of Medical Veterinary and Life Sciences > School of Health & Wellbeing > MRC/CSO SPHSU
College of Science and Engineering > School of Computing Science
Published Online:17 August 2020
Copyright Holders:Copyright © 2020 Springer Nature Switzerland AG
First Published:First published in Lecture Notes in Artificial Intelligence Diagrammatic Representation and Inference
Publisher Policy:Reproduced in accordance with the publisher copyright policy
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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
168560MRC SPHSU/GU Transfer FellowshipsLaurence MooreMedical Research Council (MRC)MC_PC_13027HW - MRC/CSO Social and Public Health Sciences Unit
Chief Scientist Office (CSO)SPHSU14
727661SPHSU Core Renewal: Complexity in Health Improvement Research ProgrammeLaurence MooreMedical Research Council (MRC)MC_UU_12017/14IHW - MRC/CSO SPHU