University of Warwick Library
When analyzing uncertain data sets, users are often interested in explanations for their observations. Explaining the causes of surprising query results allows users to better understand their data, and identify possible errors in data or queries. In this paper, the authors propose causality as a unified framework to explain query answers and non-answers, thus generalizing and extending several previously proposed definitions of provenance and missing query result explanations. Starting from the established definition of actual causes by the researcher, they propose functional causes as a refined definition of causality with several desirable properties.