A framework to improve causal inferences from visualizations using counterfactual operators

IF 1.8 4区 计算机科学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING Information Visualization Pub Date : 2024-08-06 DOI:10.1177/14738716241265120
Arran Zeyu Wang, David Borland, David Gotz
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Abstract

Exploratory data analysis of high-dimensional datasets is a crucial task for which visual analytics can be especially useful. However, the ad hoc nature of exploratory analysis can also lead users to draw incorrect causal inferences. Previous studies have demonstrated this risk and shown that integrating counterfactual concepts within visual analytics systems can improve users’ understanding of visualized data. However, effectively leveraging counterfactual concepts can be challenging, with only bespoke implementations found in prior work. Moreover, it can require expertise in both counterfactual subset analysis and visualization to implement the functionalities practically. This paper aims to help address these challenges in two ways. First, we propose an operator-based conceptual model for the use of counterfactuals that is informed by prior work in visualization research. Second, we contribute the Co-op library, an open and extensible reference implementation of this model that can support the integration of counterfactual-based subset computation with visualization systems. To evaluate the effectiveness and generalizability of Co-op, the library was used to construct two different visual analytics systems each supporting a distinct user workflow. In addition, expert interviews were conducted with professional visual analytics researchers and engineers to gain more insights regarding how Co-op could be leveraged. Finally, informed in part by these evaluation results, we distil a set of key design implications for effectively leveraging counterfactuals in future visualization systems.
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利用反事实运算符改进可视化因果推断的框架
对高维数据集进行探索性数据分析是一项至关重要的任务,可视化分析在这方面尤其有用。然而,探索性分析的临时性也可能导致用户得出错误的因果推论。以往的研究已经证明了这种风险,并表明在可视化分析系统中整合反事实概念可以提高用户对可视化数据的理解。然而,有效利用反事实概念可能具有挑战性,在之前的工作中只发现了定制的实现方法。此外,还需要反事实子集分析和可视化方面的专业知识,才能切实实现这些功能。本文旨在通过两种方式帮助应对这些挑战。首先,我们为反事实的使用提出了一个基于运算符的概念模型,该模型参考了可视化研究方面的前期工作。其次,我们贡献了 Co-op 库,它是该模型的一个开放且可扩展的参考实现,可支持基于反事实的子集计算与可视化系统的集成。为了评估 Co-op 的有效性和可推广性,我们使用该库构建了两个不同的可视化分析系统,每个系统都支持不同的用户工作流程。此外,还对专业的可视化分析研究人员和工程师进行了专家访谈,以获得更多关于如何利用 Co-op 的见解。最后,根据这些评估结果,我们提炼出一套关键的设计理念,以便在未来的可视化系统中有效利用反事实。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Information Visualization
Information Visualization COMPUTER SCIENCE, SOFTWARE ENGINEERING-
CiteScore
5.40
自引率
0.00%
发文量
16
审稿时长
>12 weeks
期刊介绍: Information Visualization is essential reading for researchers and practitioners of information visualization and is of interest to computer scientists and data analysts working on related specialisms. This journal is an international, peer-reviewed journal publishing articles on fundamental research and applications of information visualization. The journal acts as a dedicated forum for the theories, methodologies, techniques and evaluations of information visualization and its applications. The journal is a core vehicle for developing a generic research agenda for the field by identifying and developing the unique and significant aspects of information visualization. Emphasis is placed on interdisciplinary material and on the close connection between theory and practice. This journal is a member of the Committee on Publication Ethics (COPE).
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