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: heec8dd06459db069
Venue: SIGMOD
Year: 2026
Pagerank: 4.9769913e-05
Overall Rank: 10,636 | 28.52%
DOI: 10.1145/3786631
PDF: Download (CC BY-NC 4.0)

Incoming Non-self Citations Over Time

No non-self incoming citations found for this paper in this database.

Authors

1. Tal Blau (Ben Gurion University; Israel)
2. Brit Youngmann (Israel; Technion)
3. Anna Fariha (USA; University of Utah)
4. Yuval Moskovitch (Ben Gurion University; Israel)

BibTeX Citation

@inproceedings{blau_sigmod26,
        title = {{Causal Explanations for Disparate Trends: Where and Why?}},
        author = {Blau, Tal and Youngmann, Brit and Fariha, Anna and Moskovitch, Yuval},
        series = {{SIGMOD} '26},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3786631},
        url = {https://dl.acm.org/doi/10.1145/3786631},
        year = {2026}
}

Incoming Citations (Sorted by Pagerank)

Showing 0 of 0 citing papers.

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
29	Fast Algorithms for Mining Association Rules	1994	VLDB	0.00051189413
190	Scorpion: Explaining Away Outliers in Aggregate Queries	2013	VLDB	0.0002582857
395	SeeDB: Efficient Data-Driven Visualization Recommendations to Support Visual Analytics	2015	VLDB	0.00019156481
578	The Complexity of Causality and Responsibility for Query Answers and non-Answers	2011	VLDB	0.00016093679
671	A Formal Approach to Finding Explanations for Database Queries	2014	SIGMOD	0.00014948069
878	Interpretable and Informative Explanations of Outcomes	2015	VLDB	0.00013296412
925	Intelligent Rollups in Multidimensional OLAP Data	2001	VLDB	0.00013045856
984	User-adaptive exploration of multidimensional data	2000	VLDB	0.00012680398
2,222	Explaining Query Answers with Explanation-Ready Databases	2016	VLDB	8.8106741e-05
2,355	Bias in OLAP Queries: Detection, Explanation, and Removal (Or Think Twice About Your AVG-Query)	2018	SIGMOD	8.5862478e-05
3,065	Explaining Black-Box Algorithms Using Probabilistic Contrastive Counterfactuals	2021	SIGMOD	7.6876494e-05
3,263	Looking for Trouble: Analyzing Classifier Behavior via Pattern Divergence	2021	SIGMOD	7.4779877e-05
3,460	Quotient Cube: How to Summarize the Semantics of a Data Cube	2002	VLDB	7.2811286e-05
4,568	HYPER: Hypothetical Reasoning With What-If and How-To Queries Using a Probabilistic Causal Approach	2022	SIGMOD	6.5279657e-05
4,731	Interactive Summarization and Exploration of Top Aggregate Query Answers	2018	VLDB	6.4440072e-05
4,741	Going Beyond Provenance: Explaining Query Answers with Pattern-based Counterbalances	2019	SIGMOD	6.4411456e-05
4,842	High-Level Why-Not Explanations using Ontologies	2015	PODS	6.3828247e-05
4,984	XInsight: eXplainable Data Analysis Through The Lens of Causality	2023	SIGMOD	6.325863e-05
5,533	Putting Things into Context: Rich Explanations for Query Answers using Join Graphs	2021	SIGMOD	6.0906707e-05
6,186	Causal Data Integration	2023	VLDB	5.8559807e-05
6,250	RC-Index: Diversifying Answers to Range Queries	2018	VLDB	5.8333139e-05
6,438	Summarized Causal Explanations For Aggregate Views	2024	SIGMOD	5.7826256e-05
6,488	Approximate Summaries for Why and Why-not Provenance	2020	VLDB	5.7659318e-05
6,712	Selective Provenance for Datalog Programs Using Top-K Queries	2015	VLDB	5.70009e-05
7,148	Guided Exploration of Data Summaries	2022	VLDB	5.5967192e-05
8,360	FEDEX: An Explainability Framework for Data Exploration Steps	2022	VLDB	5.3456868e-05
8,890	DPXPlain: Privately Explaining Aggregate Query Answers	2023	VLDB	5.2542808e-05
9,241	Causal DAG Summarization	2025	VLDB	5.2032182e-05
9,587	On Detecting Cherry-picked Generalizations	2022	VLDB	5.154741e-05
9,604	Equivalence-Invariant Algebraic Provenance for Hyperplane Update Queries	2020	SIGMOD	5.1534211e-05
9,957	Fair and Actionable Causal Prescription Ruleset	2025	SIGMOD	5.1014161e-05
11,369	Finding Convincing Views to Endorse a Claim	2025	VLDB	4.9769913e-05

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