7 papers · 1 filter
Contextualization or Rationalization? The Effect of Causal Priors on Data Visualization Interpretation
Arran Zeyu Wang, David Borland, Estella Calcaterra +1
Understanding how individuals interpret charts is a crucial concern for visual data communication. This imperative has motivated a number of studies, including past work demonstrat…
Visual Analytics for Causal Reasoning from Real-World Health Data
Arran Zeyu Wang, David Borland, David Gotz
The increasing capture and analysis of large-scale longitudinal health data offer opportunities to improve healthcare and advance medical understanding. However, a critical gap exi…
Beyond Correlation: Incorporating Counterfactual Guidance to Better Support Exploratory Visual Analysis
Arran Zeyu Wang, David Borland, David Gotz
Providing effective guidance for users has long been an important and challenging task for efficient exploratory visual analytics, especially when selecting variables for visualiza…
Causal Priors and Their Influence on Judgements of Causality in Visualized Data
Arran Zeyu Wang, David Borland, Tabitha C. Peck +2
"Correlation does not imply causation" is a famous mantra in statistical and visual analysis. However, consumers of visualizations often draw causal conclusions when only correlati…
Selection-Bias-Corrected Visualization via Dynamic Reweighting
David Borland, Jonathan Zhang, Smiti Kaul +1
The collection and visual analysis of large-scale data from complex systems, such as electronic health records or clickstream data, has become increasingly common across a wide ran…
Visual Analysis of High-Dimensional Event Sequence Data via Dynamic Hierarchical Aggregation
David Gotz, Jonathan Zhang, Wenyuan Wang +2
Temporal event data are collected across a broad range of domains, and a variety of visual analytics techniques have been developed to empower analysts working with this form of da…