# Sufficient Explanations in Databases and their Connections to Database Repairs

arXiv: https://arxiv.org/abs/2511.15623 · HTML: https://mlsanity.com/paper/2511.15623/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 41 references that are in the map.
- First posted on arXiv 19 Nov 2025.

## Builds on (most cited first, 13 of 13)

- [Abduction-Based Explanations for Machine Learning Models](https://mlsanity.com/paper/1811.10656/) (2018)
- [ASP-Core-2 Input Language Format](https://mlsanity.com/paper/1911.04326/) (2019)
- [Explanations for Monotonic Classifiers](https://mlsanity.com/paper/2106.00154/) (2021)
- [Logic for Explainable AI](https://mlsanity.com/paper/2305.05172/) (2023)
- [From Causes for Database Queries to Repairs and Model-Based Diagnosis and Back](https://mlsanity.com/paper/1412.4311/) (2014)
- [Complexity of Consistent Query Answering in Databases under Cardinality-Based and Incremental Repair Semantics (extended version)](https://mlsanity.com/paper/1605.07159/) (2016)
- [Quantifying Causal Effects on Query Answering in Databases](https://mlsanity.com/paper/1603.02705/) (2016)
- [Axiomatic Aggregations of Abductive Explanations](https://mlsanity.com/paper/2310.03131/) (2023)
- [Causes for Query Answers from Databases: Datalog Abduction, View-Updates, and Integrity Constraints](https://mlsanity.com/paper/1611.01711/) (2016)
- [Shapley Revisited: Tractable Responsibility Measures for Query Answers](https://mlsanity.com/paper/2503.22358/) (2025)
- [The Complexity of Why-Provenance for Datalog Queries](https://mlsanity.com/paper/2303.12773/) (2023)
- [Finding Smallest Witnesses for Conjunctive Queries](https://mlsanity.com/paper/2311.18157/) (2023)
- [Causality-Based Scores Alignment in Explainable Data Management](https://mlsanity.com/paper/2503.14469/) (2025)

## Built on by (most cited first, 1 of 1)

- [Reconciling Consistency-Based Diagnosis with Actual-Causality-Based Explanations](https://mlsanity.com/paper/2605.08688/) (2026)

## Abstract (arXiv)

We investigate the notion of sufficient explanation, and a sufficiency-degree as attribution score for database tuples in relation to query answering. We also investigate and exploit connections with database repairs as used for dealing with inconsistent databases; and with causality-based necessary explanations, obtaining new computational results. We show how to use answer-set programs to specify sufficient explanations and compute sufficiency-degrees.