想用自然语言问交通因果问题？teLLMe这个新框架可以帮你，它用PC算法和DoWhy分析BDD驾驶数据，结果以Causal Card呈现，直观清晰。
交通机构拥有大量视频数据，但观测数据难以回答因果问题。teLLMe系统结合PC算法进行因果结构学习，通过bootstrap稳定性检查和DoWhy库进行效应估计。用户可用自然语言指定处理变量、结果和子群体，系统返回“Causal Card”展示效应估计、调整集和DAG支持。案例基于BDD数据集表明，该系统能发现天气、高峰时段与交通密度之间的合理关系。
teLLMe Why (Ain't Nothing but a Jam): Exploratory Causal Analysis of Urban Driving Data
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.