# Attribution-Scores in Data Management and Explainable Machine Learning

arXiv: https://arxiv.org/abs/2308.00184 · HTML: https://mlsanity.com/paper/2308.00184/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 49 references that are in the map.
- First posted on arXiv 31 Jul 2023.

## Builds on (most cited first, 19 of 19)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Actionable Recourse in Linear Classification](https://mlsanity.com/paper/1809.06514/) (2018)
- [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)
- [On Tractable Representations of Binary Neural Networks](https://mlsanity.com/paper/2004.02082/) (2020)
- [An Interpretable Model with Globally Consistent Explanations for Credit Risk](https://mlsanity.com/paper/1811.12615/) (2018)
- [On the Complexity of SHAP-Score-Based Explanations: Tractability via Knowledge Compilation and Non-Approximability Results](https://mlsanity.com/paper/2104.08015/) (2021)
- [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)
- [The Shapley Value of Inconsistency Measures for Functional Dependencies](https://mlsanity.com/paper/2009.13819/) (2020)
- [The Tractability of SHAP-Score-Based Explanations over Deterministic and Decomposable Boolean Circuits](https://mlsanity.com/paper/2007.14045/) (2020)
- [Efficient Computation of Shap Explanation Scores for Neural Network Classifiers via Knowledge Compilation](https://mlsanity.com/paper/2303.06516/) (2023)
- [From Database Repairs to Causality in Databases and Beyond](https://mlsanity.com/paper/2306.09374/) (2023)
- [Repair-Based Degrees of Database Inconsistency: Computation and Complexity](https://mlsanity.com/paper/1809.10286/) (2018)
- [Attribution-Scores and Causal Counterfactuals as Explanations in Artificial Intelligence](https://mlsanity.com/paper/2303.02829/) (2023)

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

- [The Causal-Effect Score in Data Management](https://mlsanity.com/paper/2502.02495/) (2025)
- [Reconciling Consistency-Based Diagnosis with Actual-Causality-Based Explanations](https://mlsanity.com/paper/2605.08688/) (2026)

## Abstract (arXiv)

We describe recent research on the use of actual causality in the definition of responsibility scores as explanations for query answers in databases, and for outcomes from classification models in machine learning. In the case of databases, useful connections with database repairs are illustrated and exploited. Repairs are also used to give a quantitative measure of the consistency of a database. For classification models, the responsibility score is properly extended and illustrated. The efficient computation of Shap-score is also analyzed and discussed. The emphasis is placed on work done by the author and collaborators.