  Complex, high-dimensional data is used in a wide range of domains to explore
problems and make decisions. Analysis of high-dimensional data, however, is
vulnerable to the hidden influence of confounding variables, especially as
users apply ad hoc filtering operations to visualize only specific subsets of
an entire dataset. Thus, visual data-driven analysis can mislead users and
encourage mistaken assumptions about causality or the strength of relationships
between features. This work introduces a novel visual approach designed to
reveal the presence of confounding variables via counterfactual possibilities
during visual data analysis. It is implemented in CoFact, an interactive
visualization prototype that determines and visualizes \textit{counterfactual
subsets} to better support user exploration of feature relationships. Using
publicly available datasets, we conducted a controlled user study to
demonstrate the effectiveness of our approach; the results indicate that users
exposed to counterfactual visualizations formed more careful judgments about
feature-to-outcome relationships.
