CauSumX: Summarized Causal Explanations For Group-By-Average Queries
Summary: CauSumX yields concise, causal explanations for group-by-average queries, aiding interpretation of high-dimensional results. It uses background causal knowledge and an efficient algorithm to reveal key cause-effect drivers of cross-group variation. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Nativ Levy
- 2. Michael John Cafarella
- 3. Amir Gilad
- 4. Sudeepa Roy
- 5. Brit Youngmann
Incoming Citations (Sorted by Pagerank)
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Outgoing Citations (Sorted by Pagerank)
Showing 9 of 9 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 36 | Fast Algorithms for Mining Association Rules | 1994 | VLDB | 0.00076161096 |
| 214 | Scorpion: Explaining Away Outliers in Aggregate Queries | 2013 | VLDB | 0.0003363692 |
| 942 | A Formal Approach to Finding Explanations for Database Queries | 2014 | SIGMOD | 0.00015155714 |
| 1,099 | Interpretable and Informative Explanations of Outcomes | 2015 | VLDB | 0.00014096312 |
| 1,106 | Provenance for Aggregate Queries | 2011 | PODS | 0.0001398766 |
| 2,810 | Bias in OLAP Queries: Detection, Explanation, and Removal (Or Think Twice About Your AVG-Query) | 2018 | SIGMOD | 8.0810163e-05 |
| 5,313 | XInsight: eXplainable Data Analysis Through The Lens of Causality | 2023 | SIGMOD | 5.573009e-05 |
| 5,691 | Putting Things into Context: Rich Explanations for Query Answers using Join Graphs | 2021 | SIGMOD | 5.3684557e-05 |
| 7,172 | Summarized Causal Explanations For Aggregate Views | 2024 | SIGMOD | 4.8114797e-05 |
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