Visual Analysis of Multi-Outcome Causal Graphs
Artikel i vetenskaplig tidskrift, 2025

We introduce a visual analysis method for multiple causal graphs with different outcome variables, namely, multi-outcome causal graphs. Multi-outcome causal graphs are important in healthcare for understanding multimorbidity and comorbidity. To support the visual analysis, we collaborated with medical experts to devise two comparative visualization techniques at different stages of the analysis process. First, a progressive visualization method is proposed for comparing multiple state-of-the-art causal discovery algorithms. The method can handle mixed-type datasets comprising both continuous and categorical variables and assist in the creation of a fine-tuned causal graph of a single o utcome. Second, a comparative graph layout technique and specialized visual encodings are devised for the quick comparison of multiple causal graphs. In our visual analysis approach, analysts start by building individual causal graphs for each outcome variable, and then, multi-outcome causal graphs are generated and visualized with our comparative technique for analyzing differences and commonalities of these causal graphs. Evaluation includes quantitative measurements on benchmark datasets, a case study with a medical expert, and expert user studies with real-world health research data.

comparative visualization

visual analysis in medicine

causal discovery

Causal graph visualization and visual analysis

Författare

Mengjie Fan

Peking University Health Science Center

Jinlu Yu

Student vid Chalmers

Daniel Weiskopf

Universität Stuttgart

Nan Cao

Tongji University

Huai Yu Wang

Research Center of TCM Information Engineering

Liang Zhou

Beijing University of Technology

IEEE Transactions on Visualization and Computer Graphics

1077-2626 (ISSN) 19410506 (eISSN)

Vol. 31 1 656-666

Ämneskategorier (SSIF 2025)

Datavetenskap (datalogi)

DOI

10.1109/TVCG.2024.3456346

Mer information

Senast uppdaterat

2025-03-21