teLLMe Why (Ain't Nothing but a Jam): Exploratory Causal Analysis of Urban Driving Data

Source

• Source name: arXiv
• Source kind: paper
• Authors / organization: Qiwei Li, Jorge Ortiz
• Venue: arXiv: cs.AI
• Published: 2026-07-16
• Collected: 2026-07-17
• URL: http://arxiv.org/abs/2607.15254v1
• PDF: https://arxiv.org/pdf/2607.15254v1
• DOI: UNKNOWN

Plain-Language Brief

Researchers developed teLLMe, a system that allows traffic agencies to analyze urban driving data using causal analysis methods. The system helps answer questions about the impact of various factors on traffic density, such as rain or peak hours, by using structured event tables and machine learning techniques.

What the Document Says

• The paper introduces teLLMe, a system for exploratory causal analysis of urban driving datasets.
• TeLLMe uses 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.
• The system maps natural-language questions to structured causal queries through a schema-aware LLM.
• 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 demonstrate the system's ability to surface plausible relationships involving weather, peak hours, and traffic density while making uncertainty and modeling choices explicit.

Key Concepts Mentioned

• Traffic data analysis
• Causal structure learning
• PC algorithm
• Bootstrap-based stability checks
• Linear regression
• DoWhy
• Schema-aware LLM
• Natural-language questions
• Structured causal queries
• Causal Card
• Effect estimates
• Adjustment sets
• DAG support
• Assumptions
• Uncertainty and modeling choices
• Hypothesis generation
• Expert reasoning

Claims Made by the Document

• TeLLMe can help answer causal questions about urban driving data.

Evidence Used

• The introduction and explanation of teLLMe's methods and functions.

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

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.