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

Shan Shan

Keywords: Causal Relationships, LLM-Driven Interpretation, Causality and Large Models

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.

Submission Number: 20