  As social media platforms grow, understanding the underlying reasons behind
events and statements becomes crucial for businesses, policymakers, and
researchers. This research explores the integration of Knowledge Graphs (KGs)
with Large Language Models (LLMs) to perform causal analysis of tweets dataset.
The LLM aided analysis techniques often lack depth in uncovering the causes
driving observed effects. By leveraging KGs and LLMs, which encode rich
semantic relationships and temporal information, this study aims to uncover the
complex interplay of factors influencing causal dynamics and compare the
results obtained using GPT-3.5 Turbo. We employ a Retrieval-Augmented
Generation (RAG) model, utilizing a KG stored in a Neo4j (a.k.a PRAGyan) data
format, to retrieve relevant context for causal reasoning. Our approach
demonstrates that the KG-enhanced LLM RAG can provide improved results when
compared to the baseline LLM (GPT-3.5 Turbo) model as the source corpus
increases in size. Our qualitative analysis highlights the advantages of
combining KGs with LLMs for improved interpretability and actionable insights,
facilitating informed decision-making across various domains. Whereas,
quantitative analysis using metrics such as BLEU and cosine similarity show
that our approach outperforms the baseline by 10\%.
