论文标题
探索性因果分析的视觉分析方法：勘探，验证和应用
A Visual Analytics Approach for Exploratory Causal Analysis: Exploration, Validation, and Applications
论文作者
论文摘要
利用因果关系指导决策已成为从营销和医学到教育和社会科学的各个领域的重要分析任务。尽管已经开发出强大的统计模型来从数据中推断出因果关系，但领域从业人员仍然缺乏解释因果关系并将其应用于决策过程的有效视觉界面。通过对领域专家的访谈研究，我们表征了他们当前的决策工作流程，挑战和需求。通过迭代设计过程，我们开发了一个可视化工具，该工具允许分析师在现实世界决策方案中探索，验证和应用因果关系。该工具提供了一种不确定性感知的因果图可视化，以显示从高维数据推断出的大量因果关系。除了因果图外，它还支持一组直观的用户控件，用于执行何种分析并制定行动计划。我们报告了两项营销和学生建议的案例研究，以证明用户可以有效地探索因果关系和设计行动计划以实现其目标。
Using causal relations to guide decision making has become an essential analytical task across various domains, from marketing and medicine to education and social science. While powerful statistical models have been developed for inferring causal relations from data, domain practitioners still lack effective visual interface for interpreting the causal relations and applying them in their decision-making process. Through interview studies with domain experts, we characterize their current decision-making workflows, challenges, and needs. Through an iterative design process, we developed a visualization tool that allows analysts to explore, validate, and apply causal relations in real-world decision-making scenarios. The tool provides an uncertainty-aware causal graph visualization for presenting a large set of causal relations inferred from high-dimensional data. On top of the causal graph, it supports a set of intuitive user controls for performing what-if analyses and making action plans. We report on two case studies in marketing and student advising to demonstrate that users can effectively explore causal relations and design action plans for reaching their goals.
