Designing Computational Tools for Exploring Causal Relationships in Qualitative Data

2026-02-06

AI Summary

Existing computational tools for qualitative data analysis often fall short in effectively supporting causal exploration due to insufficient contextual awareness, limited trustworthiness, or overly complex outputs. To address these limitations, this work proposes QualCausal, the first interactive causal analysis system grounded in user research–driven design principles. Developed through formative user studies, QualCausal integrates context-aware processing, cognitive scaffolding, and explainability mechanisms to facilitate efficient exploration and validation of causal hypotheses within qualitative datasets. The system enables researchers to extract causal relationships, construct interactive causal networks, and examine findings through coordinated multi-view visualizations. User evaluations demonstrate that QualCausal significantly reduces analytical burden, provides robust cognitive support, and prompts critical reflection on how computational tools can be meaningfully integrated into social science research practices, thereby bridging the gap between computational assistance and qualitative inquiry paradigms.

Technology Category

• Reasoning under Uncertainty: Causality
• Knowledge Representation and Reasoning: Qualitative Reasoning
• Machine Learning: Causal Learning

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Web Mining and Content Analysis: Web data quality in the era of algorithmically-generated content
• Social Networks and Social Media: Computational social science

Abstract

Exploring causal relationships for qualitative data analysis in HCI and social science research enables the understanding of user needs and theory building. However, current computational tools primarily characterize and categorize qualitative data; the few systems that analyze causal relationships either inadequately consider context, lack credibility, or produce overly complex outputs. We first conducted a formative study with 15 participants interested in using computational tools for exploring causal relationships in qualitative data to understand their needs and derive design guidelines. Based on these findings, we designed and implemented QualCausal, a system that extracts and illustrates causal relationships through interactive causal network construction and multi-view visualization. A feedback study (n = 15) revealed that participants valued our system for reducing the analytical burden and providing cognitive scaffolding, yet navigated how such systems fit within their established research paradigms, practices, and habits. We discuss broader implications for designing computational tools that support qualitative data analysis.

Problem

• causal relationships
• qualitative data analysis
• computational tools
• HCI
• context

Innovation

• causal relationship exploration
• qualitative data analysis
• interactive causal network
• multi-view visualization
• computational tool design