# A Visual Analytics Approach for Exploratory Causal Analysis: Exploration, Validation, and Applications

arXiv: https://arxiv.org/abs/2009.02458 · HTML: https://mlsanity.com/paper/2009.02458/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Sep 2020.

## Built on by (most cited first, 11 of 11)

- [D-BIAS: A Causality-Based Human-in-the-Loop System for Tackling Algorithmic Bias](https://mlsanity.com/paper/2208.05126/) (2022)
- [DOMINO: Visual Causal Reasoning with Time-Dependent Phenomena](https://mlsanity.com/paper/2303.06556/) (2023)
- [An Explainable AI Approach to Large Language Model Assisted Causal Model Auditing and Development](https://mlsanity.com/paper/2312.16211/) (2023)
- [Visual Analysis of Multi-outcome Causal Graphs](https://mlsanity.com/paper/2408.02679/) (2024)
- [CausalChat: Interactive Causal Model Development and Refinement Using Large Language Models](https://mlsanity.com/paper/2410.14146/) (2024)
- [VAC2: Visual Analysis of Combined Causality in Event Sequences](https://mlsanity.com/paper/2206.05420/) (2022)
- [VISPUR: Visual Aids for Identifying and Interpreting Spurious Associations in Data-Driven Decisions](https://mlsanity.com/paper/2307.14448/) (2023)
- [XplainAct: Visualization for Personalized Intervention Insights](https://mlsanity.com/paper/2507.14767/) (2025)
- [Causal-driven attribution (CDA): Estimating channel influence without user-level data](https://mlsanity.com/paper/2512.21211/) (2025)
- [Designing Computational Tools for Exploring Causal Relationships in Qualitative Data](https://mlsanity.com/paper/2602.06506/) (2026)
- [Human-Guided Causal Knowledge Injection for Virtual Cells](https://mlsanity.com/paper/2608.08430/) (2026)

## Abstract (arXiv)

Using causal relations to guide decision making has become an essential analytical task across various domains, from marketing and medicine to education and social science. While powerful statistical models have been developed for inferring causal relations from data, domain practitioners still lack effective visual interface for interpreting the causal relations and applying them in their decision-making process. Through interview studies with domain experts, we characterize their current decision-making workflows, challenges, and needs. Through an iterative design process, we developed a visualization tool that allows analysts to explore, validate, and apply causal relations in real-world decision-making scenarios. The tool provides an uncertainty-aware causal graph visualization for presenting a large set of causal relations inferred from high-dimensional data. On top of the causal graph, it supports a set of intuitive user controls for performing what-if analyses and making action plans. We report on two case studies in marketing and student advising to demonstrate that users can effectively explore causal relations and design action plans for reaching their goals.