Improving Visualization Interpretation Using Counterfactuals

TL;DR

This paper presents CoFact, a visualization tool that uses counterfactuals to reveal confounding variables in high-dimensional data, helping users make more accurate causal judgments during analysis.

Contribution

It introduces a novel counterfactual visualization approach and implements it in CoFact to improve understanding of feature relationships in high-dimensional data.

Findings

Users made more careful causal judgments with counterfactual visualizations

Counterfactuals helped identify confounding variables more effectively

The approach was validated through a controlled user study

Abstract

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.