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

Shan Shan

TL;DR

The paper tackles moving from correlation to causation in climate-change research by proposing a three-step causal inference framework that combines correlation analysis, ML-based causality discovery, and LLM-guided interpretations. It contributes a structured methodology that narrows the variable pool, constructs and prunes causal graphs via CAM pruning, and validates interpretations through LLM prompts aligned with a formal causal taxonomy and Pearl’s Causal Hierarchy. Key findings highlight strong causal effects of Access to Clean Fuels and Technologies for Cooking and Urban Population on per-capita carbon emissions, offering policy-relevant levers for emissions reduction. The work demonstrates a practical pathway for data-driven policymaking by integrating observational data with causal inference and interpretable, language-model-based insights.

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.

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

TL;DR

The paper tackles moving from correlation to causation in climate-change research by proposing a three-step causal inference framework that combines correlation analysis, ML-based causality discovery, and LLM-guided interpretations. It contributes a structured methodology that narrows the variable pool, constructs and prunes causal graphs via CAM pruning, and validates interpretations through LLM prompts aligned with a formal causal taxonomy and Pearl’s Causal Hierarchy. Key findings highlight strong causal effects of Access to Clean Fuels and Technologies for Cooking and Urban Population on per-capita carbon emissions, offering policy-relevant levers for emissions reduction. The work demonstrates a practical pathway for data-driven policymaking by integrating observational data with causal inference and interpretable, language-model-based insights.

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.

Paper Structure

This paper contains 28 sections, 9 equations, 4 figures, 1 table.

Table of Contents

  1. Introduction
  2. Preliminaries: Causal Relations
  3. Correlation Analysis: Narrowing the Variable Pool
  4. Causal Effects Estimation
  5. Causal Graph Construction and Score Matching
  6. CAM Pruning
  7. Validation: From Correlations to Causation via LLM Inquiries
  8. Taxonomy of Causality
  9. Results of Causal Relationship and Interpretations
  10. Conclusion: Evaluations and Integrations
  11. Three-step Framework for Causal Analysis
  12. Setup and Main Results
  13. Data Processing
  14. Correlation Map
  15. Ordered Feature Correlation with Carbon Emissions
  16. ...and 13 more sections

Figures (4)

  • Figure 1: Three-step Framework for Causal Analysis
  • Figure 2: Clustered Correlation Heatmap of Social Factors Influencing Carbon Emissions This heatmap illustrates the correlations between various social factors and carbon emissions, highlighting key relationships. The clustering visually groups factors with similar correlation patterns, aiding in identifying which socioeconomic indicators most strongly influence carbon emissions, thereby providing insights into the complex interplay between social behavior and climate impact. The dendrogram, shown as lines on the top and left of the heatmap, represents hierarchical clustering. It groups variables based on the similarity of correlation or distance, with shorter line heights indicating higher similarity. Variables in the rows and columns are grouped to identify clusters with closely related pairwise relationships.
  • Figure 3: Ordered Feature Correlation with Carbon Emissions. This figure shows the correlation between various social and economic factors and carbon emissions per capita, highlighting key influences such as energy use, GDP, urban population, and access to clean technologies.
  • Figure 4: Causal Relationships Among Social Factors and Carbon Emissions. This scoring map illustrates the causal relationships between various social factors and carbon emissions, highlighting key variables: access to clean fuels and technologies for cooking in rural and urban areas (EG.CFT.ACCS.RU.ZS and EG.CFT.ACCS.UR.ZS) and urban population percentage (SP.URB.TOTL.IN.ZS). These factors show strong causal effects on carbon emissions per capita, emphasizing the interconnectedness of urbanization, energy use, and climate change.