  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
outcome. 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.
