An Exploratory User Study of Visual Causality Analysis
An Exploratory User Study of Visual Causality Analysis
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DOI：
10.1111/cgf.13680
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发表时间：
2019-06
影响因子：
2.5
通讯作者：
Chi-Hsien Yen;Aditya G. Parameswaran;W. Fu
中科院分区：
文献类型：
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作者：
Chi-Hsien Yen;Aditya G. Parameswaran;W. Fu
越来越多的公众成员正在使用交互式可视化工具。但是，人们对人们如何以及如何使用可视化来推断因果关系知之甚少。根据调解因果模型，我们设计了一个分析框架，以系统地评估视觉因果推理任务中的人类绩效，策略和陷阱。我们招募了24名参与者，并要求他们使用条形图和我们可视化接口中的散点图在虚拟数据集中识别中介。结果表明，当混杂变量直接影响被分析的变量时，其响应的准确性是否显着降低。进一步的分析表明，个人可视化探索策略和界面如何影响推理性能。我们还在其因果推理过程中确定了常见的策略和陷阱。讨论了如何设计对未来视觉分析工具如何更好地支持因果推理的设计含义。
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.