This repository is a curated reading list of academic papers focused on the intersection of causality and Natural Language Processing (NLP). It serves researchers, engineers, and students interested in applying causal inference techniques to understand, interpret, and improve NLP models and tasks. The primary benefit is a structured and comprehensive overview of a rapidly evolving research area.

How It Works

The repository organizes papers into logical categories, starting with causality basics, then moving to general NLP applications (like causal reasoning, interpretability, and relation extraction), and finally covering specific application domains. It also includes links to toolboxes, foundational work, and resources from leading research labs (MPI, MILA), providing a broad yet organized view of the field.

Quick Start & Requirements

This is a reading list, not a software package. No installation or execution is required. All content consists of links to academic papers, often with direct PDF links.

Highlighted Details

• Extensive coverage of papers applying causality to Large Language Models (LLMs), including surveys on LLMs and causal inference.
• Detailed sections on formal and commonsense causal reasoning, interpretability via causal methods, and causal relation extraction.
• Includes resources from prominent researchers and institutions like Bernhard Schölkopf (MPI) and Yoshua Bengio (MILA).
• Provides links to relevant software toolboxes for causal discovery and effect estimation.

Maintenance & Community

The repository is actively updated, as indicated by the "Actively Updating" note. Contributions are welcomed via issues or pull requests. The primary contributor, Zhijing Jin, is a PhD student advised by Bernhard Schölkopf, focusing on NLP and Causality.

Licensing & Compatibility

This repository contains links to publicly available academic papers. The licensing of the papers themselves varies by publisher. The repository itself is likely under a permissive license (e.g., MIT, Apache 2.0) given its nature as a curated list, but this is not explicitly stated.

Limitations & Caveats

As a reading list, it does not provide code or executable tools. The sheer volume of papers can be overwhelming, and the organization, while good, may require users to navigate multiple levels to find specific topics. The focus is academic, with less emphasis on practical, deployable NLP solutions.