activity
20242026
most citedAn Empirical Study of Counterfactual Visualization to Support Visual Causal Inference

10 citations · 31 across the 7 of their papers we have counts for

collaborators

7 papers

cs.HC2026

Enhanced Self-Learning with Epistemologically-Informed LLM Dialogue

Yi-Fan Cao, Kento Shigyo, Yitong Gu +6

Large Language Models (LLMs) have advanced self-learning tools, enabling more personalized interactions. However, learners struggle to engage in meaningful dialogue and process com…

cs.HC2026

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…

cs.HC2025

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…

cs.HC2024★ 7 cited

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…

cs.HC2024★ 7 cited

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…

cs.HC2024★ 10 cited

An Empirical Study of Counterfactual Visualization to Support Visual Causal Inference

Arran Zeyu Wang, David Borland, David Gotz

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

cs.HC2024★ 7 cited

Using Counterfactuals to Improve Causal Inferences from Visualizations

David Borland, Arran Zeyu Wang, David Gotz

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 th…