Traditional approaches to data visualization have often focused on comparing different subsets of data, and this is reflected in the many techniques developed and evaluated over the years for visual comparison. Similarly, common workflows for exploratory visualization are built upon the idea of users interactively applying various filter and grouping mechanisms in search of new insights. This paradigm has proven effective at helping users identify correlations between variables that can inform thinking and decision-making. However, recent studies show that consumers of visualizations often draw causal conclusions even when not supported by the data. Motivated by these observations, this article highlights recent advances from a growing community of researchers exploring methods that aim to directly support visual causal inference. However, many of these approaches have their own limitations which limit their use in many real-world scenarios. This article therefore also outlines a set of key open challenges and corresponding priorities for new research to advance the state of the art in visual causal inference.

翻译：传统的可视化方法通常侧重于比较不同的数据子集，这体现在多年来为视觉比较开发并评估的多种技术中。类似地，探索性可视化的常见工作流程建立在用户通过交互式应用各种过滤和分组机制以寻找新见解的理念之上。这种范式已被证明能有效帮助用户识别变量之间的相关性，从而为思考和决策提供信息。然而，近期研究表明，即使数据不支持，可视化受众也常常得出因果结论。受这些观察的启发，本文强调了来自一个不断扩大的研究者群体的最新进展，这些研究者探索旨在直接支持视觉因果推断的方法。然而，许多这些方法自身存在局限性，限制了它们在许多实际场景中的应用。因此，本文还概述了视觉因果推断领域亟需解决的一系列关键开放挑战及相应新研究优先方向，以推动该领域技术发展。