From Correlation to Causation: Understanding Climate Change through Causal Analysis and LLM Interpretations
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866915075120431104 |
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| author | Shan, Shan |
| author_facet | Shan, Shan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_16691 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | From Correlation to Causation: Understanding Climate Change through Causal Analysis and LLM Interpretations Shan, Shan Machine Learning Computers and Society Methodology 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. |
| title | From Correlation to Causation: Understanding Climate Change through Causal Analysis and LLM Interpretations |
| topic | Machine Learning Computers and Society Methodology |
| url | https://arxiv.org/abs/2412.16691 |