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.

翻译：我们提出了一种面向不同结局变量的多因果图视觉分析方法，即多结局因果图。多结局因果图对于理解医疗健康领域中的多病共存与共病现象具有重要意义。为支持该视觉分析，我们与医学专家合作，在分析流程的不同阶段设计了两种比较可视化技术。首先，提出了一种渐进式可视化方法，用于比较多种最先进的因果发现算法。该方法可处理包含连续变量与分类变量的混合类型数据集，并辅助为单一结局创建精细调优的因果图。其次，设计了比较图布局技术与专门的可视化编码，以快速比较多个因果图。在我们的视觉分析方案中，分析者首先为每个结局变量构建个体因果图，随后生成多结局因果图，并通过比较技术对因果图的差异性与共通性进行可视化分析。评估工作包括在基准数据集上的定量测量、与医学专家的案例研究，以及基于真实健康研究数据的专家用户研究。