Current work on using visual analytics to determine causal relations among variables has mostly been based on the concept of counterfactuals. As such the derived static causal networks do not take into account the effect of time as an indicator. However, knowing the time delay of a causal relation can be crucial as it instructs how and when actions should be taken. Yet, similar to static causality, deriving causal relations from observational time-series data, as opposed to designed experiments, is not a straightforward process. It can greatly benefit from human insight to break ties and resolve errors. We hence propose a set of visual analytics methods that allow humans to participate in the discovery of causal relations associated with windows of time delay. Specifically, we leverage a well-established method, logic-based causality, to enable analysts to test the significance of potential causes and measure their influences toward a certain effect. Furthermore, since an effect can be a cause of other effects, we allow users to aggregate different temporal cause-effect relations found with our method into a visual flow diagram to enable the discovery of temporal causal networks. To demonstrate the effectiveness of our methods we constructed a prototype system named DOMINO and showcase it via a number of case studies using real-world datasets. Finally, we also used DOMINO to conduct several evaluations with human analysts from different science domains in order to gain feedback on the utility of our system in practical scenarios.

翻译：摘要：当前利用视觉分析确定变量间因果关系的工作大多基于反事实概念，由此推导出的静态因果网络未将时间作为指示因素纳入考量。然而，掌握因果关系的时滞至关重要，因为它能指导行动的方式与时机。但如同静态因果推理，基于观测性时间序列数据（而非设计实验）推导因果关系并非直接明了的过程，需要借助人类洞察力来打破僵局并解决误差。为此，我们提出一套视觉分析方法，允许人类参与发现与时间延迟窗口相关的因果关系。具体而言，我们采用逻辑因果这一成熟方法，使分析人员能够检验潜在原因的显著性，并量化其对特定效应的影响强度。此外，由于某个效应可能成为其他效应的原因，我们允许用户将方法中发现的各类时态因果关系统合为视觉流程示意图，从而揭示时态因果网络。为验证方法有效性，我们构建了名为DOMINO的原型系统，并通过基于真实数据集的多个案例进行展示。最终，我们还利用DOMINO与来自不同科学领域的人类分析师开展多项评估，以获取系统在实际应用场景中的实用性反馈。