CausalChat: Interactive Causal Model Development and Refinement Using Large Language Models

TL;DR

CausalChat is an interactive tool that uses GPT-4 to help users develop and refine causal models through conversational exploration and visual analytics, reducing reliance on large expert groups.

Contribution

It introduces a novel visual analytics interface leveraging GPT-4 for interactive causal model development, enabling users to identify causal relationships with minimal domain expertise.

Findings

Effective in diverse data contexts

User studies show improved causal understanding

Reduces need for large expert panels

Abstract

Causal networks are widely used in many fields to model the complex relationships between variables. A recent approach has sought to construct causal networks by leveraging the wisdom of crowds through the collective participation of humans. While this can yield detailed causal networks that model the underlying phenomena quite well, it requires a large number of individuals with domain understanding. We adopt a different approach: leveraging the causal knowledge that large language models, such as OpenAI's GPT-4, have learned by ingesting massive amounts of literature. Within a dedicated visual analytics interface, called CausalChat, users explore single variables or variable pairs recursively to identify causal relations, latent variables, confounders, and mediators, constructing detailed causal networks through conversation. Each probing interaction is translated into a tailored GPT-4 prompt and the response is conveyed through visual representations which are linked to the generated text for explanations. We demonstrate the functionality of CausalChat across diverse data contexts and conduct user studies involving both domain experts and laypersons.