We introduce a visualanalysis method for multiple causalgraphs with different outcome variables, namely, multi-outcome causalgraphs. Multi-outcome causalgraphs are important in healthcare for understanding multimo...
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We introduce a visualanalysis method for multiple causalgraphs with different outcome variables, namely, multi-outcome causalgraphs. Multi-outcome causalgraphs are important in healthcare for understanding multimorbidity and comorbidity. To support the visualanalysis, 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 causalgraph of a single outcome. Second, a comparative graph layout technique and specialized visual encodings are devised for the quick comparison of multiple causalgraphs. In our visualanalysis approach, analysts start by building individual causalgraphs for each outcome variable, and then, multi-outcome causalgraphs are generated and visualized with our comparative technique for analyzing differences and commonalities of these causalgraphs. Evaluation includes quantitative measurements on benchmark datasets, a case study with a medical expert, and expert user studies with real-world health research data.
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