用因果分析+大模型解读，找出影响碳排放的真正因素
From Correlation to Causation: Understanding Climate Change through Causal Analysis and LLM Interpretations
- 三步走：先找相关性，再挖掘因果，最后用大模型解释
- 识别出关键社会经济因素对碳排放的因果影响
- 适合政策制定者和气候研究者参考决策逻辑
本研究提出一种三步因果推断框架，融合相关性分析、基于机器学习的因果发现以及大语言模型（LLM）驱动的解释，识别影响碳排放的社会经济因素及其在气候变化中的作用。方法从相关性分析入手，经由因果推断，最终通过LLM生成关于气候变化背景的探究性问题，提升决策支持能力。该框架提供可适配的解决方案，助力气候相关领域的数据驱动型政策制定与战略决策，揭示气候变化领域内的因果关系。
原文摘要 · Abstract (English)
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.
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