Summary

PyWhy-LLM is an experimental Python library designed to integrate Large Language Models (LLMs) into causal analysis workflows. It aims to augment human expertise by providing LLM-powered insights, bridging knowledge gaps typically filled by domain experts, and enhancing the capabilities of the DoWhy ecosystem.

How It Works

This library leverages LLMs, such as GPT-4, to automate and suggest critical steps in causal inference. It offers modules for suggesting domain expertises, potential confounders, causal relationships (DAGs), backdoor sets, mediators, and instrumental variables. A Retrieval Augmented Generation (RAG) component, utilizing CauseNet, further enhances relationship suggestions by grounding LLM outputs in external knowledge. This approach aims to streamline the causal discovery and identification process.

Quick Start & Requirements

Installation is straightforward via pip: pip install pywhyllm. The library requires access to LLM APIs (e.g., GPT-4), implying potential API key configuration and associated costs. Specific Python versions or hardware requirements like GPUs are not detailed in the provided README. Links to detailed usage are available via "Walkthrough Notebook" and "Examples Notebook".

Highlighted Details

• Seamless integration of LLM capabilities into established causal inference pipelines.
• Automated suggestion of key causal components: confounders, mediators, IVs, and graph structures.
• RAG-based modeler for enhanced relationship discovery using CauseNet.
• Part of the broader DoWhy community, fostering ecosystem compatibility.

Maintenance & Community

Contributions are welcomed, with guidelines provided in CONTRIBUTING.md. Users can report issues or make requests by raising an issue on the project's repository. A Code of Conduct is also available.

Licensing & Compatibility

The provided README does not specify a software license. This absence requires further investigation for determining commercial use or derivative work compatibility.

Limitations & Caveats

As an experimental library, PyWhy-LLM may be subject to changes, bugs, or incomplete features. Its functionality is dependent on the performance and availability of external LLM services, introducing potential costs and reliability concerns. The effectiveness of suggestions relies heavily on the quality of the underlying LLM and the specific causal problem context.