The paper introduces the Autonomous LLM-Augmented Causal Discovery Framework (ALCM), a novel framework that integrates Large Language Models (LLMs) with conventional causal discovery algorithms to enhance the accuracy and robustness of causal graph generation. ALCM consists of three main components: causal structure learning, causal wrapper, and LLM-driven causal refiner. These components work together to generate and refine causal graphs from observational data, leveraging the strengths of both traditional causal discovery methods and LLMs. The authors evaluate ALCM using seven well-known datasets and compare its performance with conventional causal discovery algorithms and LLM-based approaches. The results demonstrate that ALCM outperforms existing methods in terms of precision, recall, F1-score, accuracy, and Normalized Hamming Distance (NHD), indicating its effectiveness in uncovering complex causal relationships. The study also highlights the potential of ALCM in handling dynamic data, detecting hidden variables, and providing more comprehensive graph model representations. Future work includes integrating knowledge graphs, Monte Carlo Tree Search (MCTS), and Retrieval-Augmented Generation (RAG) to further enhance the framework's capabilities and address issues such as LLM hallucination.The paper introduces the Autonomous LLM-Augmented Causal Discovery Framework (ALCM), a novel framework that integrates Large Language Models (LLMs) with conventional causal discovery algorithms to enhance the accuracy and robustness of causal graph generation. ALCM consists of three main components: causal structure learning, causal wrapper, and LLM-driven causal refiner. These components work together to generate and refine causal graphs from observational data, leveraging the strengths of both traditional causal discovery methods and LLMs. The authors evaluate ALCM using seven well-known datasets and compare its performance with conventional causal discovery algorithms and LLM-based approaches. The results demonstrate that ALCM outperforms existing methods in terms of precision, recall, F1-score, accuracy, and Normalized Hamming Distance (NHD), indicating its effectiveness in uncovering complex causal relationships. The study also highlights the potential of ALCM in handling dynamic data, detecting hidden variables, and providing more comprehensive graph model representations. Future work includes integrating knowledge graphs, Monte Carlo Tree Search (MCTS), and Retrieval-Augmented Generation (RAG) to further enhance the framework's capabilities and address issues such as LLM hallucination.