  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.
