Counterfactuals -- expressing what might have been true under different circumstances -- have been widely applied in statistics and machine learning to help understand causal relationships. More recently, counterfactuals have begun to emerge as a technique being applied within visualization research. However, it remains unclear to what extent counterfactuals can aid with visual data communication. In this paper, we primarily focus on assessing the quality of users' understanding of data when provided with counterfactual visualizations. We propose a preliminary model of causality comprehension by connecting theories from causal inference and visual data communication. Leveraging this model, we conducted an empirical study to explore how counterfactuals can improve users' understanding of data in static visualizations. Our results indicate that visualizing counterfactuals had a positive impact on participants' interpretations of causal relations within datasets. These results motivate a discussion of how to more effectively incorporate counterfactuals into data visualizations.

翻译：反事实——表达在不同情境下可能成立的情况——已被广泛应用于统计学和机器学习，以帮助理解因果关系。近年来，反事实开始作为一种技术出现在可视化研究领域。然而，反事实在多大程度上能够辅助视觉数据沟通仍不明确。本文主要评估用户在获得反事实可视化时对数据的理解质量。我们通过连接因果推断和视觉数据沟通的相关理论，提出了一个因果理解初步模型。基于该模型，我们进行了一项实证研究，探索反事实如何提升用户在静态可视化中对数据的理解。结果表明，可视化反事实对参与者解读数据集中的因果联系具有积极影响。这些结果引发了关于如何更有效地将反事实融入数据可视化的讨论。