An intelligent assistant for mediation analysis in visual analytics

Chi Hsien Yen, Yu Chun Yen, Wai-Tat Fu

Research output: Contribution to conferencePaper

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.

Original languageEnglish (US)
Pages432-436
Number of pages5
DOIs
StatePublished - Jan 1 2019
Event24th ACM International Conference on Intelligent User Interfaces, IUI 2019 - Marina del Ray, United States
Duration: Mar 17 2019Mar 20 2019

Conference

Conference24th ACM International Conference on Intelligent User Interfaces, IUI 2019
CountryUnited States
CityMarina del Ray
Period3/17/193/20/19

Keywords

  • Causal Reasoning
  • Intelligent Visualization Tool
  • Mediation Analysis

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction

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    Yen, C. H., Yen, Y. C., & Fu, W-T. (2019). An intelligent assistant for mediation analysis in visual analytics. 432-436. Paper presented at 24th ACM International Conference on Intelligent User Interfaces, IUI 2019, Marina del Ray, United States. https://doi.org/10.1145/3301275.3302325