Large Language Models and Causal AI Synergies

This whitepaper discusses the integration of large language models (LLMs) with causal AI, focusing on practical applications and use cases. It outlines three primary use cases: grounding LLMs to reduce inaccuracies, facilitating causal discovery through domain knowledge, and enhancing the productivity of causal data scientists. The paper emphasizes the importance of grounding LLMs with causal reasoning to improve decision-making processes by providing verifiable outputs. It introduces the concept of causal retrieval augmented generation (cRAG), which allows LLMs to perform causal calculations, thereby enhancing the reliability of their outputs. Additionally, it highlights how LLMs can assist in extracting domain knowledge for causal discovery, addressing the challenges of creating causal graphs from observational data. The document also presents the role of various agents designed to interact with users and facilitate these processes, ultimately aiming to improve the accuracy and efficiency of causal modeling in organizational settings.