From Correlation to Causation: Understanding Climate Change Through Causal Analysis and LLM Interpretations
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 climate change context. 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.
Cite
Text
Shan. "From Correlation to Causation: Understanding Climate Change Through Causal Analysis and LLM Interpretations." NeurIPS 2024 Workshops: CALM, 2024.Markdown
[Shan. "From Correlation to Causation: Understanding Climate Change Through Causal Analysis and LLM Interpretations." NeurIPS 2024 Workshops: CALM, 2024.](https://mlanthology.org/neuripsw/2024/shan2024neuripsw-correlation/)BibTeX
@inproceedings{shan2024neuripsw-correlation,
title = {{From Correlation to Causation: Understanding Climate Change Through Causal Analysis and LLM Interpretations}},
author = {Shan, Shan},
booktitle = {NeurIPS 2024 Workshops: CALM},
year = {2024},
url = {https://mlanthology.org/neuripsw/2024/shan2024neuripsw-correlation/}
}