标题： 从相关性到因果关系：通过因果分析和大语言模型解释理解气候变化 显示英文标题

标题： From Correlation to Causation: Understanding Climate Change through Causal Analysis and LLM Interpretations

摘要： 这项研究提出了一种三步因果推理框架，该框架结合了相关性分析、基于机器学习的因果发现以及LLM驱动的解释，以识别影响碳排放并导致气候变化的社会经济因素。 该方法首先识别相关性，然后进行因果分析，并通过LLM生成的关于气候变化背景的问题来增强决策制定。 所提出的框架提供了可适应的解决方案，支持数据驱动的政策制定和气候相关情境中的战略决策，揭示了气候变化领域内的因果关系。 显示英文摘要

摘要： This research presents a three-step causal inference framework that integrates correlation analysis, machine learning-based causality discovery, and LLM-driven interpretations to identify socioeconomic factors influencing carbon emissions and contributing to climate change. The approach begins with identifying correlations, progresses to causal analysis, and enhances decision making through LLM-generated inquiries about the context of climate change. The proposed framework offers adaptable solutions that support data-driven policy-making and strategic decision-making in climate-related contexts, uncovering causal relationships within the climate change domain.