Evaluating AutoEDA Without Human Raters: A Diagnostic Framework and Case Study of Structured Question Generation

Abstract

Human evaluation remains the dominant way to assess automated exploratory data analysis (AutoEDA) systems, but it is expensive, subjective, and hard to reproduce. We introduce a reproducible automatic diagnostic framework that surfaces structural and statistical failures in AutoEDA—failure modes often under-emphasized when reviewers prioritize fluent prose over breakdown validity and statistical form. The framework uses 9 metrics covering correctness, structural validity, statistical quality, subspace exploration, and question-insight alignment. We apply it to three representative pipelines: a QUIS-style structured question-guided system, ONLYSTATS (a statistics-based ablation), and a free-form agentic LLM. Across Adidas US Sales, IBM Employee Attrition, and Online Sales, structured intermediate representations show fewer column-type errors and broader subspace coverage in our runs. The QUIS-style pipeline reaches 94.0% average structural validity versus 40.0% for the agentic LLM, and discovers more subspace insights on average (84.4% vs. 37.4%). It also attains the highest Subspace Score (x=1.067), while the agentic LLM leads on Uplift Win Rate (66.7%, subspace beats global in 2 of 3 datasets). The QUIS-style pipeline discovers significant paradoxes in 2 of 3 datasets. Overall, schema-aware question guidance tracks higher structural validity and subgroup coverage than free-form prompting in our runs. More broadly, the paper contributes a reusable diagnostic framework for reproducible comparison of AutoEDA systems. Code and data are available at https://github.com/sunnydovision/EDA_Evaluation.