Causalvis: Visualizations for Causal Inference

Abstract

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.

arXiv comment20 pages, 14 figures

References in corpus (14)

• Boba: Authoring and Visualizing Multiverse Analyses
• Striking a Balance: Reader Takeaways and Preferences when Integrating Text and Charts
• Telling Stories from Computational Notebooks: AI-Assisted Presentation Slides Creation for Presenting Data Science Work
• Symphony: Composing Interactive Interfaces for Machine Learning
• Visual Causality Analysis of Event Sequence Data
• Outcome-Explorer: A Causality Guided Interactive Visual Interface for Interpretable Algorithmic Decision Making
• StickyLand: Breaking the Linear Presentation of Computational Notebooks
• An Evaluation Toolkit to Guide Model Selection and Cohort Definition in Causal Inference
• DOMINO: Visual Causal Reasoning with Time-Dependent Phenomena
• Decision-Making Under Uncertainty in Research Synthesis: Designing for the Garden of Forking Paths
• D-BIAS: A Causality-Based Human-in-the-Loop System for Tackling Algorithmic Bias
• VAINE: Visualization and AI for Natural Experiments
• NOVA: A Practical Method for Creating Notebook-Ready Visual Analytics
• Data+Shift: Supporting visual investigation of data distribution shifts by data scientists

Cited by in corpus (8)

• Visualization for Trust in Machine Learning Revisited: The State of the Field in 2023
• SuperNOVA: Design Strategies and Opportunities for Interactive Visualization in Computational Notebooks
• An Empirical Study of Counterfactual Visualization to Support Visual Causal Inference
• Causal Priors and Their Influence on Judgements of Causality in Visualized Data
• Visual Analysis of Multi-outcome Causal Graphs
• Design Concerns for Integrated Scripting and Interactive Visualization in Notebook Environments
• A Design Study Process Model for Medical Visualization
• VIGMA: An Open-Access Framework for Visual Gait and Motion Analytics