Visual Analysis of Multi-outcome Causal Graphs

TL;DR

This paper presents a visual analysis approach for multi-outcome causal graphs, enabling comparison of causal discovery algorithms and multiple graphs to aid healthcare research on multimorbidity.

Contribution

It introduces novel visualization techniques for comparing multiple causal graphs and algorithms, tailored for healthcare data analysis involving multiple outcomes.

Findings

Effective comparison of causal graphs using proposed visualization methods.

Quantitative evaluation on benchmark datasets shows improved analysis capabilities.

Case study with medical experts demonstrates practical utility in healthcare research.

Abstract

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.