# Causalvis: Visualizations for Causal Inference

## TL;DR

Causalvis is a Python visualization toolkit designed to support the entire causal inference process, facilitating analysis, communication, and collaboration through interactive modules tailored for experts.

## Contribution

The paper introduces Causalvis, a novel Python package with four interactive visualization modules specifically designed for causal inference workflows.

## Key findings

- Causalvis effectively supports iterative causal inference analysis.
- The toolkit enhances communication and collaboration among analysts.
- Expert feedback validated the usefulness of the visualization modules.

## 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.