# From Database Repairs to Causality in Databases and Beyond

arXiv: https://arxiv.org/abs/2306.09374 · HTML: https://mlsanity.com/paper/2306.09374/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 32 references that are in the map.
- First posted on arXiv 15 Jun 2023.

## Builds on (most cited first, 10 of 10)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [A Modification of the Halpern-Pearl Definition of Causality](https://mlsanity.com/paper/1505.00162/) (2015)
- [The Shapley Value of Tuples in Query Answering](https://mlsanity.com/paper/1904.08679/) (2019)
- [Declarative Approaches to Counterfactual Explanations for Classification](https://mlsanity.com/paper/2011.07423/) (2020)
- [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)
- [Causes for Query Answers from Databases: Datalog Abduction, View-Updates, and Integrity Constraints](https://mlsanity.com/paper/1611.01711/) (2016)
- [The Impact of Negation on the Complexity of the Shapley Value in Conjunctive Queries](https://mlsanity.com/paper/1912.12610/) (2019)
- [Attribution-Scores and Causal Counterfactuals as Explanations in Artificial Intelligence](https://mlsanity.com/paper/2303.02829/) (2023)

## Built on by (most cited first, 3 of 3)

- [Attribution-Scores in Data Management and Explainable Machine Learning](https://mlsanity.com/paper/2308.00184/) (2023)
- [Causality-Based Scores Alignment in Explainable Data Management](https://mlsanity.com/paper/2503.14469/) (2025)
- [Reconciling Consistency-Based Diagnosis with Actual-Causality-Based Explanations](https://mlsanity.com/paper/2605.08688/) (2026)

## 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.