From Correlation to Causation: Understanding Climate Change through Causal Analysis and LLM Interpretations
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2024
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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.| Reference Key |
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| Authors | Shan Shan |
| Journal | arXiv |
| Year | 2024 |
| DOI | DOI not found |
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