Causal inference is a statistical paradigm for quantifying causal effects using observational
data. It is a complex process, requiring multiple steps, iterations, and collaborations with …

Researchers have derived many theoretical models for specifying users' insights as they
interact with a visualization system. These representations are essential for understanding …

Understanding how helpful a visualization is from experimental results is difficult because
the observed performance is confounded with aspects of the study design, such as how …

Visual analytics (VA) tools support data exploration by helping analysts quickly and
iteratively generate views of data which reveal interesting patterns. However, these tools …

Counterfactuals–expressing what might have been true under different circumstances–have
been widely applied in statistics and machine learning to help understand causal …

Exploratory data analysis of high-dimensional datasets is a crucial task for which visual
analytics can be especially useful. However, the ad hoc nature of exploratory analysis can …

“Correlation does not imply causation” is a famous mantra in statistical and visual analysis.
However, consumers of visualizations often draw causal conclusions when only correlations …

Existing data visualization design guidelines focus primarily on constructing grammatically-
correct visualizations that faithfully convey the values and relationships in the underlying …

A growing body of work draws on feminist thinking to challenge assumptions about how
people engage with and use visualizations. This work draws on feminist values, driving …

Big data and machine learning tools have jointly empowered humans in making data-driven
decisions. However, many of them capture empirical associations that might be spurious …