发表机构
Rutgers University（罗格斯大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究城市驾驶数据因果问题，提出teLLMe系统，结合多种方法从结构化事件表出发，通过模式感知大语言模型映射问题，返回‘因果卡片’总结相关内容，能揭示合理关系，用于假设生成和专家推理。
AI 中文摘要
交通机构现在能够获取大量源自视频的数据以研究安全和拥堵情况。但多数数据是观测性的且无干预收集，使得诸如‘降雨如何改变交通密度？’这类因果问题难以回答。我们提出了teLLMe，一个用于城市驾驶数据集探索性因果分析的系统。该系统从由行车记录仪注释构建的结构化事件表出发，将因果结构学习与PC算法、基于自助法的稳定性检查以及使用线性回归和DoWhy的特定查询效应估计相结合。自然语言问题通过模式感知大语言模型映射到结构化因果查询，用户能指定处理、结果和子群体。teLLMe返回一张‘因果卡片’，总结效应估计、调整集、有向无环图支持和假设，并附带简短的自然语言解释。对源自BDD的交通事件的案例研究表明，该系统能揭示涉及天气、高峰时段和交通密度的合理关系，同时明确不确定性和建模选择。该系统被设计为一个用于假设生成和专家推理的工具，而非确定性因果声明的来源。
英文摘要
Traffic agencies now have access to large volumes of video-derived data for studying safety and congestion. Most of these data are observational and collected without interventions, which makes causal questions such as "How would rain change traffic density?" difficult to answer. We present teLLMe, a system for exploratory causal analysis of urban driving datasets. The system starts from a structured event table built from dashcam annotations and combines causal structure learning with the PC algorithm, bootstrap-based stability checks, and query-specific effect estimation using linear regression and DoWhy. Natural-language questions are mapped to structured causal queries through a schema-aware LLM, enabling users to specify treatments, outcomes, and subpopulations. teLLMe returns a "Causal Card" that summarizes effect estimates, adjustment sets, DAG support, and assumptions, followed by a short natural-language explanation. Case studies on BDD-derived traffic events show that the system can surface plausible relationships involving weather, peak hours, and traffic density, while making uncertainty and modeling choices explicit. The system is designed as a tool for hypothesis generation and expert reasoning rather than a source of definitive causal claims.
CommentsAccepted at the NeurIPS 2025 Workshop on UrbanAI. 6 pages
