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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
4.9605343e-05
Overall Rank
10,697 | 28.30%
DOI
10.1145/3786631
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(CC BY-NC 4.0)
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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}
}
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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
0.00051028332
191
Scorpion: Explaining Away Outliers in Aggregate Queries 2013
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0.0002578784
395
SeeDB: Efficient Data-Driven Visualization Recommendations to Support Visual Analytics 2015
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0.00019125046
581
The Complexity of Causality and Responsibility for Query Answers and non-Answers 2011
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0.00016042765
672
A Formal Approach to Finding Explanations for Database Queries 2014
SIGMOD
0.00014903109
879
Interpretable and Informative Explanations of Outcomes 2015
VLDB
0.00013255435
929
Intelligent Rollups in Multidimensional OLAP Data 2001
VLDB
0.000130092
986
User-adaptive exploration of multidimensional data 2000
VLDB
0.00012645438
2,233
Explaining Query Answers with Explanation-Ready Databases 2016
VLDB
8.7844607e-05
2,361
Bias in OLAP Queries: Detection, Explanation, and Removal (Or Think Twice About Your AVG-Query) 2018
SIGMOD
8.5605216e-05
3,074
Explaining Black-Box Algorithms Using Probabilistic Contrastive Counterfactuals 2021
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7.6622293e-05
3,276
Looking for Trouble: Analyzing Classifier Behavior via Pattern Divergence 2021
SIGMOD
7.4532608e-05
3,472
Quotient Cube: How to Summarize the Semantics of a Data Cube 2002
VLDB
7.2574793e-05
4,577
HYPER: Hypothetical Reasoning With What-If and How-To Queries Using a Probabilistic Causal Approach 2022
SIGMOD
6.5111757e-05
4,742
Going Beyond Provenance: Explaining Query Answers with Pattern-based Counterbalances 2019
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6.4285195e-05
4,752
Interactive Summarization and Exploration of Top Aggregate Query Answers 2018
VLDB
6.4226993e-05
4,865
High-Level Why-Not Explanations using Ontologies 2015
PODS
6.3619008e-05
5,008
XInsight: eXplainable Data Analysis Through The Lens of Causality 2023
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6.3049458e-05
5,556
Putting Things into Context: Rich Explanations for Query Answers using Join Graphs 2021
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6.0705311e-05
6,196
Causal Data Integration 2023
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5.8366172e-05
6,262
RC-Index: Diversifying Answers to Range Queries 2018
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5.8140253e-05
6,450
Summarized Causal Explanations For Aggregate Views 2024
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5.7635046e-05
6,502
Approximate Summaries for Why and Why-not Provenance 2020
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5.7468661e-05
6,728
Selective Provenance for Datalog Programs Using Top-K Queries 2015
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5.6812425e-05
7,173
Guided Exploration of Data Summaries 2022
VLDB
5.578213e-05
8,375
FEDEX: An Explainability Framework for Data Exploration Steps 2022
VLDB
5.3280107e-05
8,910
DPXPlain: Privately Explaining Aggregate Query Answers 2023
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5.2369069e-05
9,265
Causal DAG Summarization 2025
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9,612
On Detecting Cherry-picked Generalizations 2022
VLDB
5.1376962e-05
9,630
Equivalence-Invariant Algebraic Provenance for Hyperplane Update Queries 2020
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9,990
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5.0845476e-05
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