teLLMe

teLLMe: LLM-powered exploratory causal analysis system for urban driving data

Researchers introduce teLLMe, a system that combines causal structure learning (PC algorithm, DoWhy) with a schema-aware LLM to answer natural-language causal queries over urban driving datasets derived from dashcam annotations. Users pose questions like 'How would rain change traffic density?' and receive a structured 'Causal Card' summarizing effect estimates, adjustment sets, DAG support, and uncertainty. Case studies on BDD-derived traffic events demonstrate plausible causal relationships involving weather and peak hours. The system is framed as a hypothesis-generation tool rather than a definitive causal inference engine.