III: Medium: Counterfactual-Based Supports For Visual Causal Inference

III：媒介：基于反事实的视觉因果推理支持

• 批准号: 2211845
• 负责人: David Gotz
• 金额: $ 120万
• 依托单位: University of North Carolina at Chapel Hill
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2211845&HistoricalAwards=false
• 关键词: III; Medium; Counterfactual; Based; Supports

Data visualization is a critical and ubiquitous tool used to support data analysis tasks across a variety of domains. Visualizations are valued for their ability to “show the data” graphically, rather than using letters and numbers, in a way that enables users to assign meaning to what they see. This in turn helps users analyze complex data, discover new insights, make data-driven decisions, and communicate with other people about their findings. The correctness of these findings is therefore clearly contingent upon the correctness of the inferences that users make when viewing or interacting with a data visualization tool. However, recent studies have shown that people often interpret visualized patterns as indicators of causal relationships between variables in their data even when no causal relationships exist. The result is that visualizations can dramatically mislead users into drawing erroneous conclusions. This project develops a new approach to visualization, based on the concept of counterfactual reasoning, designed to help users draw more accurate and generalizable inferences when analyzing data using visualization tools. The project's results, including open-source software, are intended to be broadly applicable across domains. In addition, the project will be evaluated with data and users in the population health domain with the potential to contribute to improvements to human health.More specifically, this project will develop a set of innovative counterfactual-centered methods for visualization. In recognition of users' natural tendency to draw causal inferences about data while looking at data visualizations, these methods will directly aim to mitigate risks of drawing erroneous conclusions while amplifying users' ability to robustly discover patterns that are more likely to be indicators of statistically supported causal interactions. Building upon the principles of counterfactual reasoning, this project will achieve three key aims. First, methods will be developed to enhance traditional filter-driven visualizations with comparisons against counterfactual subsets. The goal is to provide users with the information required to make more robust conclusions from visualizing data. Second, methods will be developed to leverage statistics derived from these counterfactual subsets to help guide user's exploratory activity with the aim of increasing efficiency of discovery. Third, a workflow for identifying and accounting for secondary variables that correlate with those used for counterfactual comparison will be developed. The project will result in the design and development of new computational methods and user workflows, open-source software implementing these contributions, and evaluation studies that will characterize the efficacy of these counterfactual-based techniques.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

数据可视化是一种关键且普遍存在的工具，用于支持各种域的数据分析任务。可视化的价值是因为它们可以以图形方式“显示数据”而不是使用字母和数字，以使用户能够为他们看到的内容分配含义。反过来，这有助于用户分析复杂的数据，发现新的见解，做出数据驱动的决策以及与其他人就他们的发现进行沟通。因此，这些发现的正确性清楚地表明了用户在查看或与数据可视化工具交互时所做的推论的正确性。但是，最近的研究表明，即使不存在因果关系，人们经常将可视化模式解释为变量之间因果关系的指标。结果是可视化可以极大地误导用户，以得出错误的结论。该项目基于反事实推理的概念开发了一种新的可视化方法，旨在帮助用户使用可视化工具分析数据时更准确和可推广的推论。该项目的结果，包括开源软件，旨在在范围内广泛适用。此外，该项目将通过人口健康领域中的数据和用户进行评估，有可能有助于改善人类健康。更具体地说，该项目将开发一系列创新的以反事实为中心的可视化方法。为了认识到用户在查看数据可视化时绘制有关数据的因果信息的自然趋势，这些方法将直接旨在减轻得出错误的结论的风险，同时放大用户坚固地发现模式的能力，这些模式更可能是统计上支持的因果关系的指标。基于反事实推理的原则，该项目将实现三个关键目标。首先，将开发方法来增强传统的滤镜驱动可视化，并与反事实子集进行比较。目的是为用户提供从可视化数据得出更强大结论所需的信息。其次，将开发方法来利用这些反事实子集得出的统计数据，以帮助指导用户的探索活动，以提高发现效率。第三，将开发与与反事实比较相关的辅助变量识别和考虑辅助变量的工作流程。该项目将导致新的计算方法和用户工作流的设计和开发，实施这些贡献的开源软件以及将表征这些基于反事实技术的效率的评估研究。该奖项反映了NSF的法定任务，并被认为是通过基金会的智力功能和广泛影响的评估来审查CRITERIA的评估。