This survey explores the integration of causal inference with large language models (LLMs) to enhance their reasoning, fairness, safety, and explainability. LLMs, with their strong reasoning capabilities, can aid in causal discovery and effect estimation, while causal inference can improve LLMs by addressing issues like fairness, safety, and multimodality. The survey discusses how causal methods can be applied to measure and improve LLM reasoning, address fairness and safety concerns, enhance explainability, and handle multimodal data. It also examines how LLMs can contribute to causal inference by enabling causal discovery and treatment effect estimation. The paper reviews recent advancements in LLMs, including their structure, training, and applications, and highlights how causal inference can be used to improve their performance. It also discusses the challenges in applying causal inference to LLMs, such as domain shift and long-tail bias, and proposes solutions like counterfactual reasoning and causal graph abstraction. The survey concludes with a discussion on future directions for research in this area.This survey explores the integration of causal inference with large language models (LLMs) to enhance their reasoning, fairness, safety, and explainability. LLMs, with their strong reasoning capabilities, can aid in causal discovery and effect estimation, while causal inference can improve LLMs by addressing issues like fairness, safety, and multimodality. The survey discusses how causal methods can be applied to measure and improve LLM reasoning, address fairness and safety concerns, enhance explainability, and handle multimodal data. It also examines how LLMs can contribute to causal inference by enabling causal discovery and treatment effect estimation. The paper reviews recent advancements in LLMs, including their structure, training, and applications, and highlights how causal inference can be used to improve their performance. It also discusses the challenges in applying causal inference to LLMs, such as domain shift and long-tail bias, and proposes solutions like counterfactual reasoning and causal graph abstraction. The survey concludes with a discussion on future directions for research in this area.