Causal Explanations for Disparate Trends: Where and Why?

Summary: ExDis finds where disparities between two groups are strongest/reversed by mining subpopulations and the factors causally driving them. Key novelty: actionable, interpretable causal explanations for disparate trends, beyond correlational subgroup discovery, with an efficient optimization/algorithmic framework. (summarized by gpt-5.4-mini on Apr 11 2026)

Paper ID: 7457
Venue: SIGMOD
Year: 2026
Pagerank: 4.1945683e-05
Overall Rank: 10,147 | 29.41%
DOI: 10.1145/3786631

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Rank	Citing Paper	Year	Venue	Pagerank

Outgoing Citations (Sorted by Pagerank)

Showing 32 of 32 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
460	SeeDB: Efficient Data-Driven Visualization Recommendations to Support Visual Analytics	2015	VLDB	0.00022516069
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
1,119	The Complexity of Causality and Responsibility for Query Answers and non-Answers	2011	VLDB	0.0001386199
1,137	User-adaptive exploration of multidimensional data	2000	VLDB	0.00013730532
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
2,923	Explaining Black-Box Algorithms Using Probabilistic Contrastive Counterfactuals	2021	SIGMOD	7.8953538e-05
3,162	Looking for Trouble: Analyzing Classifier Behavior via Pattern Divergence	2021	SIGMOD	7.4589576e-05
3,590	Quotient Cube: How to Summarize the Semantics of a Data Cube	2002	VLDB	6.9421381e-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
6,662	Selective Provenance for Datalog Programs Using Top-K Queries	2015	VLDB	4.9704872e-05
6,696	Approximate Summaries for Why and Why-not Provenance	2020	VLDB	4.9581958e-05
7,101	RC-Index: Diversifying Answers to Range Queries	2018	VLDB	4.8322751e-05
7,172	Summarized Causal Explanations For Aggregate Views	2024	SIGMOD	4.8114797e-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
9,179	Equivalence-Invariant Algebraic Provenance for Hyperplane Update Queries	2020	SIGMOD	4.3820222e-05
9,644	Fair and Actionable Causal Prescription Ruleset	2025	SIGMOD	4.3109001e-05
9,766	DPXPlain: Privately Explaining Aggregate Query Answers	2023	VLDB	4.2856106e-05
10,581	Causal DAG Summarization	2025	VLDB	4.1945683e-05
10,740	Finding Convincing Views to Endorse a Claim	2025	VLDB	4.1945683e-05
11,419	On Detecting Cherry-picked Generalizations	2022	VLDB	4.1945683e-05

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