CauSumX: Summarized Causal Explanations For Group-By-Average Queries
Name
3722212.3725088.pdf
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1.08 MB
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Adobe PDF
Checksum (MD5)
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Author(s)
Levy, Nativ
Cafarella, Michael
Gilad, Amir
Roy, Sudeepa
Youngmann, Brit
Date Issued
June 22, 2025
Publisher
ACM|Companion of the 2025 International Conference on Management of Data
Citation
Nativ Levy, Michael Cafarella, Amir Gilad, Sudeepa Roy, and Brit Youngmann. 2025. CauSumX: Summarized Causal Explanations For Group-By-Average Queries. In Companion of the 2025 International Conference on Management of Data (SIGMOD/PODS '25). Association for Computing Machinery, New York, NY, USA, 159–162.
Version
Final published version
Abstract
Group-by-average SQL queries are a cornerstone of data analysis, often employed to uncover patterns and trends within datasets. However, interpreting the results of these queries can be challenging and time-intensive, particularly when working with large, high-dimensional datasets. Automating the generation of explanations for such queries can greatly enhance analysts' ability to derive meaningful insights while reducing human effort. Effective explanations must balance succinctness and depth, offering insights into different patterns across aggregate results, while crucially reflecting cause-effect relationships rather than mere correlations. This ensures that users can make informed, data-driven decisions grounded in reality. In this demonstration, we present CauSumX, a system that produces concise and causal explanations for group-by-average queries. Leveraging background causal knowledge, CauSumX identifies the key causal factors driving variations in the outcome variable across different groups. The system employs an efficient algorithm based on a recently published paper. We will demonstrate the utility of CauSumX for generating useful summarized causal explanations by interacting with the SIGMOD'25 participants, who will act as data analysts aiming to explain their query results.
Description
SIGMOD-Companion ’25, Berlin, Germany
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.1145/3722212.3725088
