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

数据可视化是一种关键且无处不在的工具，用于支持跨各种领域的数据分析任务。可视化的价值在于其以图形方式“显示数据”的能力，而不是使用字母和数字，以使用户能够为他们所看到的内容赋予意义。这反过来又帮助用户分析复杂的数据，发现新的见解，做出数据驱动的决策，并与其他人交流他们的发现。因此，这些发现的正确性显然取决于用户在查看或与数据可视化工具交互时所做推断的正确性。然而，最近的研究表明，即使不存在因果关系，人们也经常将可视化模式解释为数据中变量之间因果关系的指标。结果是，可视化会极大地误导用户得出错误的结论。该项目开发了一种新的可视化方法，基于反事实推理的概念，旨在帮助用户在使用可视化工具分析数据时得出更准确和可推广的推论。该项目的成果，包括开源软件，旨在广泛应用于各个领域。此外，将利用人口健康领域的数据和用户对该项目进行评价，这些数据和用户可能有助于改善人类健康。更具体地说，该项目将开发一套创新的以反事实为中心的可视化方法。认识到用户在查看数据可视化时对数据进行因果推断的自然倾向，这些方法将直接旨在降低得出错误结论的风险，同时增强用户强有力地发现模式的能力，这些模式更有可能成为统计支持的因果交互的指标。在反事实推理原理的基础上，该项目将实现三个关键目标。首先，将开发方法，通过与反事实子集的比较来增强传统的过滤器驱动的可视化。目标是为用户提供从可视化数据中得出更可靠结论所需的信息。其次，将开发方法来利用来自这些反事实子集的统计数据来帮助指导用户的探索活动，以提高发现效率。第三，将开发一个用于识别和核算与反事实比较相关的次要变量的工作流程。该项目将导致新的计算方法和用户工作流程的设计和开发，实现这些贡献的开源软件，以及将表征这些基于反事实的技术的有效性的评估研究。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.