探索性因果分析的可视化分析方法：探索、验证和应用
A Visual Analytics Approach for Exploratory Causal Analysis: Exploration, Validation, and Applications
摘要：
使用因果关系来指导决策已经成为各个领域的重要分析任务，从营销和医学到教育和社会科学。虽然已经开发了强大的统计模型来从数据中推断因果关系，但领域从业者仍然缺乏有效的视觉界面来解释因果关系并将其应用于决策过程。通过与领域专家的访谈研究，我们描述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，允许分析师在现实世界的决策场景中探索，验证和应用因果关系。该工具提供了一个不确定性感知的因果图可视化，用于呈现从高维数据推断的大量因果关系。在因果关系图之上，它支持一组直观的用户控件，用于执行假设分析和制定行动计划。我们报告的两个案例研究，在市场营销和学生咨询，以证明用户可以有效地探索因果关系和设计行动计划，以达到他们的目标。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
