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 study- ing safety and congestion. Most of these data are observational and collected with- out 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 learn- ing 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 relation- ships 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.