An intelligent assistant for mediation analysis in visual analytics
Chi-Hsien Yen, Yu-Chun Yen, Wai-Tat Fu
IUI '19: 24th International Conference on Intelligent User Interfaces
Session: Agent-based IUIs
Abstract
Mediation analysis is commonly performed using regressions or Bayesian network analysis in statistics, psychology, and health science; however, it is not effectively supported in existing visualization tools. The lack of assistance poses great risks when people use visualizations to explore causal relationships and make data-driven decisions, as spurious correlations or seemingly conflicting visual patterns might occur. In this paper, we focused on the causal reasoning task over three variables and investigated how an interface could help users reason more efficiently. We developed an interface that facilitates two processes involved in causal reasoning: 1) detecting inconsistent trends, which guides users' attention to important visual evidence, and 2) interpreting visualizations, by providing assisting visual cues and allowing users to compare key visualizations side by side. Our preliminary study showed that the features are potentially beneficial. We discuss design implications and how the features could be generalized for more complex causal analysis.
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Recorded at the 24th International Conference on Intelligent User Interfaces, Los Angeles, USA, March 16 - 20 2019
