On Explaining Confounding Bias

Brit Youngmann, Michael Cafarella, Yuval Moskovitch, Babak Salimi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


When analyzing large datasets, analysts are often interested in the explanations for unexpected results produced by their queries. In this work, we focus on aggregate SQL queries that expose correlations in the data. A major challenge that hinders the interpretation of such queries is confounding bias, which can lead to an unexpected correlation. We generate explanations in terms of a set of potential confounding variables that explain the unexpected correlation observed in a query. We propose to mine candidate confounding variables from external sources since, in many real-life scenarios, the explanations are not solely contained in the input data. We present an efficient algorithm that finds a concise subset of attributes (mined from external sources and the input dataset) that explain the unexpected correlation. This algorithm is embodied in a system called MESA. We demonstrate experimentally over multiple real-life datasets and through a user study that our approach generates insightful explanations, outperforming existing methods even when are given with the extracted attributes. We further demonstrate the robustness of our system to missing data and the ability of MESA to handle input datasets containing millions of tuples and an extensive search space of candidate confounding attributes.

Original languageEnglish
Title of host publicationProceedings - 2023 IEEE 39th International Conference on Data Engineering, ICDE 2023
PublisherInstitute of Electrical and Electronics Engineers
Number of pages14
ISBN (Electronic)9798350322279
StatePublished - 1 Jan 2023
Event39th IEEE International Conference on Data Engineering, ICDE 2023 - Anaheim, United States
Duration: 3 Apr 20237 Apr 2023

Publication series

NameProceedings - International Conference on Data Engineering
ISSN (Print)1084-4627


Conference39th IEEE International Conference on Data Engineering, ICDE 2023
Country/TerritoryUnited States


  • Conditional Mutual Information
  • Confounding Bias
  • Knowledge Graphs
  • Query Results Explanation
  • SQL

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Information Systems


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