对话式多智能体系统，让非专业用户也能一键完成因果推断分析。
CausalAgent: A Conversational Multi-Agent System for End-to-End Causal Inference
- 通过多智能体协作与自然语言交互，自动完成从数据清洗到报告生成的全流程。
- 支持交互式可视化，确保分析过程透明可解释，提升结果可信度。
- 适合医疗、经济等领域的研究人员快速开展因果分析，无需编程基础。
因果推断在医疗、经济和社会科学中具有重要价值。然而传统因果分析流程技术门槛高，研究者需兼具统计学与计算机科学知识，手动选择算法、处理数据质量问题并解读复杂结果。为此，我们提出CausalAgent，一种面向端到端因果推断的对话式多智能体系统。该系统创新性融合多智能体系统（MAS）、检索增强生成（RAG）与模型上下文协议（MCP），通过自然语言交互实现从数据清洗、因果结构学习、偏差校正到报告生成的全自动化。用户仅需上传数据集并以自然语言提问，即可获得严谨且可交互的分析报告。作为新型以人为中心的人机协同范式，CausalAgent显式建模分析流程，借助交互式可视化显著降低因果分析门槛，同时保障过程的严谨性与可解释性。
原文摘要 · Abstract (English)
Causal inference holds immense value in fields such as healthcare, economics, and social sciences. However, traditional causal analysis workflows impose significant technical barriers, requiring researchers to possess dual backgrounds in statistics and computer science, while manually selecting algorithms, handling data quality issues, and interpreting complex results. To address these challenges, we propose CausalAgent, a conversational multi-agent system for end-to-end causal inference. The system innovatively integrates Multi-Agent Systems (MAS), Retrieval-Augmented Generation (RAG), and the Model Context Protocol (MCP) to achieve automation from data cleaning and causal structure learning to bias correction and report generation through natural language interaction. Users need only upload a dataset and pose questions in natural language to receive a rigorous, interactive analysis report. As a novel user-centered human-AI collaboration paradigm, CausalAgent explicitly models the analysis workflow. By leveraging interactive visualizations, it significantly lowers the barrier to entry for causal analysis while ensuring the rigor and interpretability of the process.
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