Causal-RAG: Causally-Augmented Retrieval for Hallucination-Free Clinical Decision Support in Low-Resource Settings

This research addresses a critical challenge in using artificial intelligence for healthcare in low-resource settings: the tendency of AI models to produce confident but incorrect information, a phenomenon known as hallucination. We propose Causal-RAG, a novel framework that enhances standard Retrieval-Augmented Generation (RAG) by integrating principles of causal inference. The goal is to ground the AI's responses in robust, causally-relevant evidence rather than mere correlations. We built a prototype and tested it on a clinical question-answering task. Our findings reveal a fundamental trade-off: while a standard RAG system achieved high accuracy but displayed a dangerous 'yes' bias, our Causal-RAG approach successfully reduced this overconfidence, prioritizing safety. This work establishes a foundation for developing more trustworthy and reliable AI decision-support tools for clinical environments where data and expertise are scarce.