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    An Exploratory User Study of Visual Causality Analysis

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    Date
    2019
    Author
    Yen, Chi-Hsien Eric
    Parameswaran, Aditya
    Fu, Wai-Tat
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    Abstract
    Interactive visualization tools are being used by an increasing number of members of the general public; however, little is known about how, and how well, people use visualizations to infer causality. Adapted from the mediation causal model, we designed an analytic framework to systematically evaluate human performance, strategies, and pitfalls in a visual causal reasoning task. We recruited 24 participants and asked them to identify the mediators in a fictitious dataset using bar charts and scatter plots within our visualization interface. The results showed that the accuracy of their responses as to whether a variable is a mediator significantly decreased when a confounding variable directly influenced the variable being analyzed. Further analysis demonstrated how individual visualization exploration strategies and interfaces might influence reasoning performance. We also identified common strategies and pitfalls in their causal reasoning processes. Design implications for how future visual analytics tools can be designed to better support causal inference are discussed.
    BibTeX
    @article {10.1111:cgf.13680,
    journal = {Computer Graphics Forum},
    title = {{An Exploratory User Study of Visual Causality Analysis}},
    author = {Yen, Chi-Hsien Eric and Parameswaran, Aditya and Fu, Wai-Tat},
    year = {2019},
    publisher = {The Eurographics Association and John Wiley & Sons Ltd.},
    ISSN = {1467-8659},
    DOI = {10.1111/cgf.13680}
    }
    URI
    https://doi.org/10.1111/cgf.13680
    https://diglib.eg.org:443/handle/10.1111/cgf13680
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    Eurographics Association copyright © 2013 - 2022 
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    Theme by @mire NV
    System hosted at  Graz University of Technology.
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