C ausal RAG : Integrating Causal Graphs into Retrieval-Augmented Generation (2025)

Abstract: Large language models (LLMs) have revolutionized natural language processing (NLP), particularly through Retrieval-Augmented Generation (RAG), which enhances LLM capabilities by integrating external knowledge. However, traditional RAG systems face critical limitations, including disrupted contextual integrity due to text chunking, and over-reliance on semantic similarity for retrieval. To address these issues, we propose CausalRAG, a novel framework that incorporates causal graphs into the retrieval process. By constructing and tracing causal relationships, CausalRAG preserves contextual continuity and improves retrieval precision, leading to more accurate and interpretable responses. We evaluate CausalRAG against regular RAG and graph-based RAG approaches, demonstrating its superiority across multiple metrics. Our findings suggest that grounding retrieval in causal reasoning provides a promising approach to knowledge-intensive tasks.

## introduction the rapid advancements in large language models (llms) have revolutionized the field of natural language processing (nlp), enabling a wide range of applications @xcite @xcite . however, their reliance on pre-trained knowledge limits their ability to integrate and reason over dynamically updated external information, particularly in knowledge intensive domains such as academic research. retrieval-augmented generation (rag) has emerged as a promising framework to address this limitation @xcite , combining retrieval mechanisms with generative capabilities to enhance contextual understanding and response quality. recent research has focused on improving rag along two primary directions: 1) enhancing re-trieval efficiency and integration mechanisms by designing more adaptive and dynamic retrieval frameworks @xcite @xcite ; 2) improving the representation of external knowledge to facilitate retrieval and reasoning, with graph-based rags being a dominant approach @xcite @xcite . despite these advancements, existing rag architectures still face critical limitations that impact retrieval quality and response accuracy, primarily due to three key issues: 1) disruption of contextual integrity caused by the text chunking design; 2) reliance on semantic similarity rather than causal relevance for retrieval; and 3) a lack of accuracy in selecting truly relevant documents. through a combination of theoretical analysis and empirical evaluation, we rethink the limitations of current rag systems by introducing a novel perspective based on context recall and precision metrics. our findings reveal that both regular and graph-based rags struggle not only to retrieve truly grounded context but also to accurately discern the relationship between retrieved content and the user query. we identify this fundamental issue as one primary reason why llms in rag frameworks often generate seemingly relevant yet shallow responses that lack essential details. to address these gaps, we introduce causal-rag, a novel rag framework that integrates causal graphs to enhance retrieval accuracy and reasoning performance. unlike regular and graph-based rags, causalrag explicitly identifies causal relationships within external knowledge, preserving contextual coherence while capturing underlying cause-effect dependencies. by ensuring that retrieved documents are both relevant and causally grounded, causalrag enables the generation of more contextually rich and causally detailed responses. this approach not only improves retrieval effectiveness but also mitigates hallucinations and enhances answer faithfulness. we evaluate causalrag on datasets from diverse domains and across varying context lengths, comparing its performance with regular rag and other graph-based rag frameworks. our experiments assess performance across three key metrics: answer faithfulness, context recall, and context precision. results demonstrate that causal-rag achieves superior performance across different contexts. additionally, we conduct a case study and a parameter analysis to further examine our framework, analyzing and providing insights that contribute to ongoing research in rag. the contributions of this work are threefold: ## why regular rag fails in providing accurate responses in this section, through both analytical and experimental investigations, we identify three fundamental limitations of regular rag and rethink its design by examining its three core elements-user query, retrieved context, and response-through a novel perspective based on precision and recall. ## methodology in this section, we introduce our proposed framework-causalrag-which integrates rag with causality to overcome the limitations of existing rag systems. overall, causalrag constructs a text-based graph from uploaded documents and discovers causal paths among nodes to guide retrieval (figure 2 ). by embedding the user query, matching relevant nodes, and expanding them along causally linked paths, the system generates a causal summary that serves as the retrieval context. this approach enables the framework to preserve contextual coherence and retrieve more targeted and causally relevant evidence. we now describe each step of the framework in detail. ## experiment to evaluate the effectiveness of causalrag, we conduct a series of experiments comparing it with regular rag and other competitive graph-based rags across multiple performance metrics to ensure a comprehensive assessment. in addition, we present a case study that explicitly compares the retrieval processes of different rag variants, and a parameter study to further examine the behavior and performance of causalrag.