A Visual Analytics Approach for Exploratory Causal Analysis: Exploration, Validation, and Applications
This addresses the need for better causal analysis tools in domains like marketing and education, though it is incremental as it builds on existing statistical models with a new interface.
The paper tackles the problem of domain practitioners lacking effective visual interfaces for interpreting and applying causal relations in decision-making, resulting in a visualization tool that enables users to explore, validate, and design action plans, as demonstrated in case studies in marketing and student advising.
Using causal relations to guide decision making has become an essential analytical task across various domains, from marketing and medicine to education and social science. While powerful statistical models have been developed for inferring causal relations from data, domain practitioners still lack effective visual interface for interpreting the causal relations and applying them in their decision-making process. Through interview studies with domain experts, we characterize their current decision-making workflows, challenges, and needs. Through an iterative design process, we developed a visualization tool that allows analysts to explore, validate, and apply causal relations in real-world decision-making scenarios. The tool provides an uncertainty-aware causal graph visualization for presenting a large set of causal relations inferred from high-dimensional data. On top of the causal graph, it supports a set of intuitive user controls for performing what-if analyses and making action plans. We report on two case studies in marketing and student advising to demonstrate that users can effectively explore causal relations and design action plans for reaching their goals.