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: 6880
Venue: SIGMOD
Year: 2024
Pagerank: 4.8114797e-05
Overall Rank: 7,172 | 50.11%
DOI: 10.1145/3639328

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 10 of 10 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
9,644	Fair and Actionable Causal Prescription Ruleset	2025	SIGMOD	4.3109001e-05
10,101	Privacy-preserving and Verifiable Causal Prescriptive Analytics	2026	SIGMOD	4.1945683e-05
10,147	Causal Explanations for Disparate Trends: Where and Why?	2026	SIGMOD	4.1945683e-05
10,213	Stress-Testing Causal Claims via Cardinality Repairs	2026	SIGMOD	4.1945683e-05
10,427	CausaLens: A System for Summarizing Causal DAGs	2025	SIGMOD	4.1945683e-05
10,429	CauSumX: Summarized Causal Explanations For Group-By-Average Queries	2025	SIGMOD	4.1945683e-05
10,581	Causal DAG Summarization	2025	VLDB	4.1945683e-05
10,715	What If: Causal Analysis with Graph Databases	2025	VLDB	4.1945683e-05
10,725	Suna: Scalable Causal Confounder Discovery over Relational Data	2025	VLDB	4.1945683e-05
10,875	SDEcho: Efficient Explanation of Aggregated Sequence Difference	2025	VLDB	4.1945683e-05

Outgoing Citations (Sorted by Pagerank)

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
36	Fast Algorithms for Mining Association Rules	1994	VLDB	0.00076161096
214	Scorpion: Explaining Away Outliers in Aggregate Queries	2013	VLDB	0.0003363692
487	Why Not?	2009	SIGMOD	0.00022050218
942	A Formal Approach to Finding Explanations for Database Queries	2014	SIGMOD	0.00015155714
1,000	Intelligent Rollups in Multidimensional OLAP Data	2001	VLDB	0.00014709252
1,099	Interpretable and Informative Explanations of Outcomes	2015	VLDB	0.00014096312
1,106	Provenance for Aggregate Queries	2011	PODS	0.0001398766
1,119	The Complexity of Causality and Responsibility for Query Answers and non-Answers	2011	VLDB	0.0001386199
1,449	Causal Relational Learning	2020	SIGMOD	0.0001193267
2,074	The Generalized MDL Approach for Summarization	2002	VLDB	9.6134804e-05
2,649	Explaining Query Answers with Explanation-Ready Databases	2016	VLDB	8.3719123e-05
2,810	Bias in OLAP Queries: Detection, Explanation, and Removal (Or Think Twice About Your AVG-Query)	2018	SIGMOD	8.0810163e-05
3,027	The Impact of Negation on the Complexity of the Shapley Value in Conjunctive Queries	2020	PODS	7.6842412e-05
3,590	Quotient Cube: How to Summarize the Semantics of a Data Cube	2002	VLDB	6.9421381e-05
4,221	MDL Summarization with Holes	2005	VLDB	6.348988e-05
4,565	Constructing and Exploring Composite Items	2010	SIGMOD	6.081125e-05
4,614	Interactive Summarization and Exploration of Top Aggregate Query Answers	2018	VLDB	6.0467204e-05
5,191	Going Beyond Provenance: Explaining Query Answers with Pattern-based Counterbalances	2019	SIGMOD	5.6378768e-05
5,313	XInsight: eXplainable Data Analysis Through The Lens of Causality	2023	SIGMOD	5.573009e-05
5,418	High-Level Why-Not Explanations using Ontologies	2015	PODS	5.5178123e-05
5,607	HYPER: Hypothetical Reasoning With What-If and How-To Queries Using a Probabilistic Causal Approach	2022	SIGMOD	5.4137872e-05
5,691	Putting Things into Context: Rich Explanations for Query Answers using Join Graphs	2021	SIGMOD	5.3684557e-05
6,449	Causal Data Integration	2023	VLDB	5.0587746e-05
7,222	Guided Exploration of Data Summaries	2022	VLDB	4.797186e-05
8,388	FEDEX: An Explainability Framework for Data Exploration Steps	2022	VLDB	4.5297787e-05
9,766	DPXPlain: Privately Explaining Aggregate Query Answers	2023	VLDB	4.2856106e-05
11,419	On Detecting Cherry-picked Generalizations	2022	VLDB	4.1945683e-05

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