From Database Repairs to Causality in Databases and Beyond
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Abstract (arXiv)
We describe some recent approaches to score-based explanations for query answers in databases. The focus is on work done by the author and collaborators. Special emphasis is placed on the use of counterfactual reasoning for score specification and computation. Several examples that illustrate the flexibility of these methods are shown.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 10 of its 32 references that are in the map. First posted on arXiv 15 Jun 2023.
Builds on
10 of 32 references.
References · 10
- 2017A Unified Approach to Interpreting Model Predictionscited by 6,488
- 2015A Modification of the Halpern-Pearl Definition of Causalitycited by 47
- 2019The Shapley Value of Tuples in Query Answeringcited by 23
- 2020Declarative Approaches to Counterfactual Explanations for Classificationcited by 14
- 2014From Causes for Database Queries to Repairs and Model-Based Diagnosis and Backcited by 13
- 2016Complexity of Consistent Query Answering in Databases under Cardinality-Based and Incremental Repair Semantics (extended version)cited by 12
- 2016Quantifying Causal Effects on Query Answering in Databasescited by 10
- 2016Causes for Query Answers from Databases: Datalog Abduction, View-Updates, and Integrity Constraintscited by 8
- 2019The Impact of Negation on the Complexity of the Shapley Value in Conjunctive Queriescited by 6
- 2023Attribution-Scores and Causal Counterfactuals as Explanations in Artificial Intelligencecited by 2
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Citing papers per year: 2023: 1; 2024: 0; 2025: 1; 2026: 1.
3 papers.