# Causalvis: Visualizations for Causal Inference

arXiv: https://arxiv.org/abs/2303.00617 · HTML: https://mlsanity.com/paper/2303.00617/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Mar 2023.

## Built on by (most cited first, 8 of 8)

- [Visual Analysis of Multi-outcome Causal Graphs](https://mlsanity.com/paper/2408.02679/) (2024)
- [SuperNOVA: Design Strategies and Opportunities for Interactive Visualization in Computational Notebooks](https://mlsanity.com/paper/2305.03039/) (2023)
- [Visualization for Trust in Machine Learning Revisited: The State of the Field in 2023](https://mlsanity.com/paper/2403.12005/) (2024)
- [CausalChat: Interactive Causal Model Development and Refinement Using Large Language Models](https://mlsanity.com/paper/2410.14146/) (2024)
- [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)
- [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)

Causal inference is a statistical paradigm for quantifying causal effects using observational data. It is a complex process, requiring multiple steps, iterations, and collaborations with domain experts. Analysts often rely on visualizations to evaluate the accuracy of each step. However, existing visualization toolkits are not designed to support the entire causal inference process within computational environments familiar to analysts. In this paper, we address this gap with Causalvis, a Python visualization package for causal inference. Working closely with causal inference experts, we adopted an iterative design process to develop four interactive visualization modules to support causal inference analysis tasks. The modules are then presented back to the experts for feedback and evaluation. We found that Causalvis effectively supported the iterative causal inference process. We discuss the implications of our findings for designing visualizations for causal inference, particularly for tasks of communication and collaboration.