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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. 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