# Designing Computational Tools for Exploring Causal Relationships in Qualitative Data

arXiv: https://arxiv.org/abs/2602.06506 · HTML: https://mlsanity.com/paper/2602.06506/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 33 of its 143 references that are in the map.
- First posted on arXiv 6 Feb 2026.

## Builds on (most cited first, 30 of 33)

- [Causal Reasoning and Large Language Models: Opening a New Frontier for Causality](https://mlsanity.com/paper/2305.00050/) (2023)
- [Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyond](https://mlsanity.com/paper/2109.00725/) (2021)
- [ChainForge: A Visual Toolkit for Prompt Engineering and LLM Hypothesis Testing](https://mlsanity.com/paper/2309.09128/) (2023)
- [Concept Induction: Analyzing Unstructured Text with High-Level Concepts Using LLooM](https://mlsanity.com/paper/2404.12259/) (2024)
- [LLM-in-the-loop: Leveraging Large Language Model for Thematic Analysis](https://mlsanity.com/paper/2310.15100/) (2023)
- [How to Make Causal Inferences Using Texts](https://mlsanity.com/paper/1802.02163/) (2018)
- [CollabCoder: A Lower-barrier, Rigorous Workflow for Inductive Collaborative Qualitative Analysis with Large Language Models](https://mlsanity.com/paper/2304.07366/) (2023)
- [CoAIcoder: Examining the Effectiveness of AI-assisted Human-to-Human Collaboration in Qualitative Analysis](https://mlsanity.com/paper/2304.05560/) (2023)
- [Can ChatGPT be used to generate scientific hypotheses?](https://mlsanity.com/paper/2304.12208/) (2023)
- [Zero-shot Causal Graph Extrapolation from Text via LLMs](https://mlsanity.com/paper/2312.14670/) (2023)
- [Automating psychological hypothesis generation with AI: when large language models meet causal graph](https://mlsanity.com/paper/2402.14424/) (2024)
- [Visual Causality Analysis of Event Sequence Data](https://mlsanity.com/paper/2009.00219/) (2020)
- [Outcome-Explorer: A Causality Guided Interactive Visual Interface for Interpretable Algorithmic Decision Making](https://mlsanity.com/paper/2101.00633/) (2021)
- [Scholastic: Graphical Human-Al Collaboration for Inductive and Interpretive Text Analysis](https://mlsanity.com/paper/2208.06133/) (2022)
- [Document-level Causal Relation Extraction with Knowledge-guided Binary Question Answering](https://mlsanity.com/paper/2410.04752/) (2024)
- [A Visual Analytics Approach for Exploratory Causal Analysis: Exploration, Validation, and Applications](https://mlsanity.com/paper/2009.02458/) (2020)
- [Supporting Serendipity: Opportunities and Challenges for Human-AI Collaboration in Qualitative Analysis](https://mlsanity.com/paper/2102.03702/) (2021)
- [Illusion of Causality in Visualized Data](https://mlsanity.com/paper/1908.00215/) (2019)
- [Causalvis: Visualizations for Causal Inference](https://mlsanity.com/paper/2303.00617/) (2023)
- [Harnessing ChatGPT for thematic analysis: Are we ready?](https://mlsanity.com/paper/2310.14545/) (2023)
- [From Text to Map: A System Dynamics Bot for Constructing Causal Loop Diagrams](https://mlsanity.com/paper/2402.11400/) (2024)
- [ScholarMate: A Mixed-Initiative Tool for Qualitative Knowledge Work and Information Sensemaking](https://mlsanity.com/paper/2504.14406/) (2025)
- [What is Stigma Attributed to? A Theory-Grounded, Expert-Annotated Interview Corpus for Demystifying Mental-Health Stigma](https://mlsanity.com/paper/2505.12727/) (2025)
- [DOMINO: Visual Causal Reasoning with Time-Dependent Phenomena](https://mlsanity.com/paper/2303.06556/) (2023)
- [Automating the Information Extraction from Semi-Structured Interview Transcripts](https://mlsanity.com/paper/2403.04819/) (2024)
- [Visual Analysis of Multi-outcome Causal Graphs](https://mlsanity.com/paper/2408.02679/) (2024)
- [Deconstructing Depression Stigma: Integrating AI-driven Data Collection and Analysis with Causal Knowledge Graphs](https://mlsanity.com/paper/2502.06075/) (2025)
- [CausalFlow: Visual Analytics of Causality in Event Sequences](https://mlsanity.com/paper/2008.11899/) (2020)
- [Human-AI Collaboration in Thematic Analysis using ChatGPT: A User Study and Design Recommendations](https://mlsanity.com/paper/2311.03999/) (2023)
- [A Mixed-Initiative Visual Analytics Approach for Qualitative Causal Modeling](https://mlsanity.com/paper/2109.03669/) (2021)

## Abstract (arXiv)

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.