Causal Support: Modeling Causal Inferences with Visualizations
Causal Support: Modeling Causal Inferences with Visualizations
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DOI：
10.1109/tvcg.2021.3114824
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发表时间：
2021-07
影响因子：
5.2
通讯作者：
Alex Kale;Yifan Wu;J. Hullman
中科院分区：
文献类型：
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作者：
Alex Kale;Yifan Wu;J. Hullman
分析师经常对可能的数据生成模型进行视觉因果推断。然而，可视化分析（VA）软件往往会将这些模型隐含在分析师的脑海中，这使人们对非正式视觉“见解”的统计有效性产生怀疑。我们正式评估质量的因果推理可视化采用因果支持贝叶斯认知模型，学习的概率替代因果解释给出一些数据作为因果推理的规范性基准。我们贡献了两个实验来评估众包工作者如何检测（1）治疗效果和（2）混淆关系。我们发现，图表用户的因果推理往往是不敏感的样本大小，使他们偏离我们的规范基准。虽然在可视化中交互式交叉过滤数据可以提高敏感性，但平均而言，用户使用普通可视化的表现并不比使用文本列联表的表现更好。这些实验证明了因果支持的效用，作为一个评估框架，在VA推理和点的机会，使分析师的心理模型更明确的VA软件。
Analysts often make visual causal inferences about possible data-generating models. However, visual analytics (VA) software tends to leave these models implicit in the mind of the analyst, which casts doubt on the statistical validity of informal visual “insights”. We formally evaluate the quality of causal inferences from visualizations by adopting causal support–a Bayesian cognition model that learns the probability of alternative causal explanations given some data–as a normative benchmark for causal inferences. We contribute two experiments assessing how well crowdworkers can detect (1) a treatment effect and (2) a confounding relationship. We find that chart users' causal inferences tend to be insensitive to sample size such that they deviate from our normative benchmark. While interactively cross-filtering data in visualizations can improve sensitivity, on average users do not perform reliably better with common visualizations than they do with textual contingency tables. These experiments demonstrate the utility of causal support as an evaluation framework for inferences in VA and point to opportunities to make analysts' mental models more explicit in VA software.