This repository provides a curated, regularly updated collection of research papers focused on causality and machine learning, sourced from arXiv. It serves as a valuable resource for researchers, academics, and practitioners in fields like artificial intelligence, statistics, and data science who are interested in the intersection of these two domains. The primary benefit is easy access to a broad spectrum of relevant literature, facilitating literature reviews and staying abreast of the latest advancements.

How It Works

The project functions as a dynamic bibliography, indexing papers based on their submission date and keywords related to causality and machine learning. It leverages the arXiv API to fetch new submissions and categorize them. The collection is presented in a tabular format, linking directly to the abstract and PDF of each paper, enabling quick browsing and retrieval of relevant research.

Quick Start & Requirements

This repository is a curated list of papers and does not require installation or execution. Users can directly access the papers via the provided links.

Highlighted Details

• Comprehensive collection of causality and machine learning papers.
• Regularly updated with new submissions from arXiv.
• Direct links to paper abstracts and PDFs for easy access.
• Facilitates literature review and research discovery.

Maintenance & Community

The repository is maintained by logangraham. Information on community interaction or a specific roadmap is not provided in the README.

Licensing & Compatibility

The repository itself is likely under a permissive license (e.g., MIT, Apache), but the content consists of research papers, each with its own copyright and distribution terms as governed by arXiv and the respective publishers.

Limitations & Caveats

This repository is a curated list and does not provide any code, tools, or frameworks for performing causal inference or machine learning tasks. Its utility is solely for literature discovery.