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

• URL: http://arxiv.org/abs/2412.16691v1
• Date: Sat, 21 Dec 2024 16:33:07 GMT
• Title: From Correlation to Causation: Understanding Climate Change through Causal Analysis and LLM Interpretations
• Authors: Shan Shan,
• Abstract summary: This research presents a three-step causal inference framework that integrates correlation analysis, machine learning-based causality discovery, and LLM-driven interpretations. The proposed framework offers solutions that support data-driven policy-making and strategic decision-making in climate-related contexts.
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• Abstract: 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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