Summarized Causal Explanations For Aggregate Views
Author(s)
Youngmann, Brit
Cafarella, Michael
Gilad, Amir
Roy, Sudeepa
Date Issued
March 12, 2024
Journal
Proceedings of the ACM on Management of Data
Publisher
Association for Computing Machinery
Citation
Brit Youngmann, Michael Cafarella, Amir Gilad, and Sudeepa Roy. 2024. Summarized Causal Explanations
For Aggregate Views. Proc. ACM Manag. Data 2, 1 (SIGMOD), Article 71 (February 2024), 27 pages.
Version
Final published version
Abstract
SQL queries with group-by and average are frequently used and plotted as bar charts in several data analysis applications. Understanding the reasons behind the results in such an aggregate view may be a highly non-trivial and time-consuming task, especially for large datasets with multiple attributes. Hence, generating automated explanations for aggregate views can allow users to gain better insights into the results while saving time in data analysis. When providing explanations for such views, it is paramount to
ensure that they are succinct yet comprehensive, reveal different types of insights that hold for different aggregate answers in the
view, and, most importantly, they reflect reality and arm users to make informed data-driven decisions, i.e., the explanations do not
only consider correlations but are causal. In this paper, we present CauSumX, a framework for generating summarized causal explanations for the entire aggregate view. Using background knowledge captured in a causal DAG, CauSumX finds the most effective causal treatments for different groups in the view. We formally define the framework and the optimization problem, study its complexity, and devise an efficient algorithm using the Apriori algorithm, LP rounding, and several optimizations. We experimentally show that our system generates useful summarized causal explanations compared to prior work and scales well for large high-dimensional data.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
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Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/3639328
