Causal MAS: A Survey of Large Language Model Architectures for Discovery and Effect Estimation

Large Language Models (LLMs) have demonstrated remarkable capabilities in various reasoning and generation tasks. However, their proficiency in complex causal reasoning, discovery, and estimation remains an area of active development, often hindered by issues like hallucination, reliance on spurious correlations, and difficulties in handling nuanced, domain - specific, or personalized causal relationships. Multi - agent system s, leveraging the collaborative or specialized abilities of multiple LLM - based agents, are emerging as a powerful paradigm to address these limitations. This review paper explores the burgeoning field of causal multi - agent LLMs. We examine how these system s are designed to tackle different facets of causality, including causal reasoning and counterfactual analysis, causal discovery from data, and the estimation of causal effects. We delve into the diverse architectural patterns and interaction protocols emp loyed, from pipeline - based processing and debate frameworks to simulation environments and iterative refinement loops. Furthermore, we discuss the evaluation methodologies, benchmarks, and diverse application domains where causal multi - agent LLMs are makin g an impact, including scientific discovery, healthcare, fact - checking, and personalized systems. Finally, we highlight the persistent challenges, open research questions, and promising future directions in this synergistic field, aiming to provide a compr ehensive overview of its current state and potential trajectory. 1. Introduction