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: 6881
Venue: SIGMOD
Year: 2024
Pagerank: 4.8068645e-05
Overall Rank: 7,172 | 50.16%
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	4.3067693e-05
10,101	Privacy-preserving and Verifiable Causal Prescriptive Analytics	2026	SIGMOD	4.1905499e-05
10,147	Causal Explanations for Disparate Trends: Where and Why?	2026	SIGMOD	4.1905499e-05
10,213	Stress-Testing Causal Claims via Cardinality Repairs	2026	SIGMOD	4.1905499e-05
10,437	CausaLens: A System for Summarizing Causal DAGs	2025	SIGMOD	4.1905499e-05
10,439	CauSumX: Summarized Causal Explanations For Group-By-Average Queries	2025	SIGMOD	4.1905499e-05
10,590	Causal DAG Summarization	2025	VLDB	4.1905499e-05
10,722	What If: Causal Analysis with Graph Databases	2025	VLDB	4.1905499e-05
10,732	Suna: Scalable Causal Confounder Discovery over Relational Data	2025	VLDB	4.1905499e-05
10,879	SDEcho: Efficient Explanation of Aggregated Sequence Difference	2025	VLDB	4.1905499e-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	0.00076114894
213	Scorpion: Explaining Away Outliers in Aggregate Queries	2013	VLDB	0.0003371037
487	Why Not?	2009	SIGMOD	0.00022030123
943	A Formal Approach to Finding Explanations for Database Queries	2014	SIGMOD	0.00015140995
1,001	Intelligent Rollups in Multidimensional OLAP Data	2001	VLDB	0.00014709145
1,096	Interpretable and Informative Explanations of Outcomes	2015	VLDB	0.00014088686
1,106	Provenance for Aggregate Queries	2011	PODS	0.00013976386
1,119	The Complexity of Causality and Responsibility for Query Answers and non-Answers	2011	VLDB	0.00013851012
1,454	Causal Relational Learning	2020	SIGMOD	0.00011921443
2,079	The Generalized MDL Approach for Summarization	2002	VLDB	9.5972684e-05
2,655	Explaining Query Answers with Explanation-Ready Databases	2016	VLDB	8.3638668e-05
2,816	Bias in OLAP Queries: Detection, Explanation, and Removal (Or Think Twice About Your AVG-Query)	2018	SIGMOD	8.0747683e-05
3,030	The Impact of Negation on the Complexity of the Shapley Value in Conjunctive Queries	2020	PODS	7.676832e-05
3,585	Quotient Cube: How to Summarize the Semantics of a Data Cube	2002	VLDB	6.9408892e-05
4,218	MDL Summarization with Holes	2005	VLDB	6.3436747e-05
4,561	Constructing and Exploring Composite Items	2010	SIGMOD	6.0758276e-05
4,608	Interactive Summarization and Exploration of Top Aggregate Query Answers	2018	VLDB	6.046643e-05
5,192	Going Beyond Provenance: Explaining Query Answers with Pattern-based Counterbalances	2019	SIGMOD	5.6324589e-05
5,321	XInsight: eXplainable Data Analysis Through The Lens of Causality	2023	SIGMOD	5.5676564e-05
5,428	High-Level Why-Not Explanations using Ontologies	2015	PODS	5.5125084e-05
5,617	HYPER: Hypothetical Reasoning With What-If and How-To Queries Using a Probabilistic Causal Approach	2022	SIGMOD	5.4085897e-05
5,706	Putting Things into Context: Rich Explanations for Query Answers using Join Graphs	2021	SIGMOD	5.3633001e-05
6,445	Causal Data Integration	2023	VLDB	5.0539192e-05
7,200	Guided Exploration of Data Summaries	2022	VLDB	4.7980895e-05
8,388	FEDEX: An Explainability Framework for Data Exploration Steps	2022	VLDB	4.525436e-05
9,768	DPXPlain: Privately Explaining Aggregate Query Answers	2023	VLDB	4.2815042e-05
11,422	On Detecting Cherry-picked Generalizations	2022	VLDB	4.1905499e-05

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