# Score-Based Explanations in Data Management and Machine Learning: An Answer-Set Programming Approach to Counterfactual Analysis

arXiv: https://arxiv.org/abs/2106.10562 · HTML: https://mlsanity.com/paper/2106.10562/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 59 references that are in the map.
- First posted on arXiv 19 Jun 2021.

## Builds on (most cited first, 17 of 17)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [On the Tractability of SHAP Explanations](https://mlsanity.com/paper/2009.08634/) (2020)
- [A Modification of the Halpern-Pearl Definition of Causality](https://mlsanity.com/paper/1505.00162/) (2015)
- [An Interpretable Model with Globally Consistent Explanations for Credit Risk](https://mlsanity.com/paper/1811.12615/) (2018)
- [Causality-based Explanation of Classification Outcomes](https://mlsanity.com/paper/2003.06868/) (2020)
- [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)
- [An ASP-Based Approach to Counterfactual Explanations for Classification](https://mlsanity.com/paper/2004.13237/) (2020)
- [Causes for Query Answers from Databases: Datalog Abduction, View-Updates, and Integrity Constraints](https://mlsanity.com/paper/1611.01711/) (2016)
- [The Shapley Value of Inconsistency Measures for Functional Dependencies](https://mlsanity.com/paper/2009.13819/) (2020)
- [The Impact of Negation on the Complexity of the Shapley Value in Conjunctive Queries](https://mlsanity.com/paper/1912.12610/) (2019)
- [The Tractability of SHAP-Score-Based Explanations over Deterministic and Decomposable Boolean Circuits](https://mlsanity.com/paper/2007.14045/) (2020)
- [Repair-Based Degrees of Database Inconsistency: Computation and Complexity](https://mlsanity.com/paper/1809.10286/) (2018)
- [Explaining Predictions from Tree-based Boosting Ensembles](https://mlsanity.com/paper/1907.02582/) (2019)

## Built on by (most cited first, 2 of 2)

- [Attribution-Scores and Causal Counterfactuals as Explanations in Artificial Intelligence](https://mlsanity.com/paper/2303.02829/) (2023)
- [Answer-Set Programs for Repair Updates and Counterfactual Interventions](https://mlsanity.com/paper/2209.12110/) (2022)

## Abstract (arXiv)

We describe some recent approaches to score-based explanations for query answers in databases and outcomes from classification models in machine learning. The focus is on work done by the author and collaborators. Special emphasis is placed on declarative approaches based on answer-set programming to the use of counterfactual reasoning for score specification and computation. Several examples that illustrate the flexibility of these methods are shown.