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Summarized Causal Explanations For Aggregate Views
Summary: CauSumX generates summarized causal explanations for entire aggregate views (group-by averages) via a causal DAG. It uses an optimization with Apriori+LP rounding to select group-specific causal treatments; scalable to high-dimensional data and outperforms prior work in usefulness and realism.
(summarized by gpt-5-nano on Feb 09 2026)
Paper ID
h829d8e94eb69e145
Venue
SIGMOD
Year
2024
Pagerank
5.7853643e-05
Overall Rank
6,436 | 56.73%
DOI
10.1145/3639328
Incoming Non-self Citations Over Time
BibTeX Citation
@inproceedings{youngmann_sigmod24,
title = {{Summarized Causal Explanations For Aggregate Views}},
author = {Youngmann, Brit and Cafarella, Michael and Gilad, Amir and Roy, Sudeepa},
series = {{SIGMOD} '24},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3639328},
url = {https://dl.acm.org/doi/10.1145/3639328},
year = {2024}
}
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Showing 27 of 27 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
Rank
Cited Paper
Year
Venue
Pagerank
29
Fast Algorithms for Mining Association Rules 1994
VLDB
0.0005121339
189
Scorpion: Explaining Away Outliers in Aggregate Queries 2013
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0.00025840026
391
Why Not? 2009
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0.00019238698
578
The Complexity of Causality and Responsibility for Query Answers and non-Answers 2011
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0.00016096504
670
A Formal Approach to Finding Explanations for Database Queries 2014
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0.00014954494
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Provenance for Aggregate Queries 2011
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0.00013666629
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Interpretable and Informative Explanations of Outcomes 2015
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0.00013302631
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Intelligent Rollups in Multidimensional OLAP Data 2001
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Explaining Query Answers with Explanation-Ready Databases 2016
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Causal Relational Learning 2020
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Bias in OLAP Queries: Detection, Explanation, and Removal (Or Think Twice About Your AVG-Query) 2018
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The Generalized MDL Approach for Summarization 2002
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8.4746361e-05
2,722
The Impact of Negation on the Complexity of the Shapley Value in Conjunctive Queries 2020
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Quotient Cube: How to Summarize the Semantics of a Data Cube 2002
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7.2845441e-05
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MDL Summarization with Holes 2005
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6.6890966e-05
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HYPER: Hypothetical Reasoning With What-If and How-To Queries Using a Probabilistic Causal Approach 2022
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Interactive Summarization and Exploration of Top Aggregate Query Answers 2018
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Going Beyond Provenance: Explaining Query Answers with Pattern-based Counterbalances 2019
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High-Level Why-Not Explanations using Ontologies 2015
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Constructing and Exploring Composite Items 2010
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XInsight: eXplainable Data Analysis Through The Lens of Causality 2023
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Putting Things into Context: Rich Explanations for Query Answers using Join Graphs 2021
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Causal Data Integration 2023
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Guided Exploration of Data Summaries 2022
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FEDEX: An Explainability Framework for Data Exploration Steps 2022
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DPXPlain: Privately Explaining Aggregate Query Answers 2023
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