  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.
