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

TL;DR

This paper introduces a three-step causal inference framework combining correlation analysis, machine learning causality discovery, and large language model interpretations to better understand socioeconomic factors impacting climate change.

Contribution

It presents a novel integrated framework that advances climate change analysis by combining causal inference with LLM-driven contextual understanding.

Findings

Identifies key socioeconomic factors influencing carbon emissions

Uncovers causal relationships within climate change data

Supports data-driven policy-making in climate strategies

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.