An AI Agent for Automated Causal Inference in Epidemiology

Abstract Objective To address the inefficiency, subjectivity, and high expertise barrier of traditional epidemiological causal inference, this study designed, developed, and validated an AI-powered agent (EpiCausalX Agent) to automate the end-to-end workflow. It integrates cross-database literature retrieval, intelligent causal reasoning, and Directed Acyclic Graph (DAG) visualization to provide a reliable, accessible tool for researchers. Materials and Methods Built on the LangChain 1.0 framework with a layered design (Agent/Tool/Storage/Utility Layers), the agent uses the DeepSeek V3.2 LLM and ReAct paradigm for dynamic task orchestration. Four specialized tools were integrated including multi-database retrieval with 7 databases, causal inference based on Hill’s criteria and DAG logic, automated DAG drawing using NetworkX and Matplotlib, and clinical standard query. Performance was validated via unit tests, workflow verification, and usability testing. Results The agent achieved full-process automation. It efficiently retrieves and synthesizes literature, automatically identifies confounders and mediators, and generates standardized interactive DAGs. It produces evidence-based, traceable conclusions aligned with established epidemiological knowledge. Its user-friendly natural language interface enables seamless use by non-technical researchers who complete task initiation quickly without operational confusion. The agent is publicly available on WeChat Mini Program for easy access. Conclusion EpiCausalX Agent advances intelligent, automated epidemiological research. By integrating domain expertise with AI agent technology, it overcomes limitations of manual methods and general LLMs to provide a specialized, verifiable, efficient solution. It has broad applications in observational research, clinical study design, and education to enhance productivity and lower barriers to rigorous causal analysis.