# Attribution-Scores and Causal Counterfactuals as Explanations in Artificial Intelligence

arXiv: https://arxiv.org/abs/2303.02829 · HTML: https://mlsanity.com/paper/2303.02829/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 66 references that are in the map.
- First posted on arXiv 6 Mar 2023.

## Builds on (most cited first, 24 of 24)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [A Survey Of Methods For Explaining Black Box Models](https://mlsanity.com/paper/1802.01933/) (2018)
- [Actionable Recourse in Linear Classification](https://mlsanity.com/paper/1809.06514/) (2018)
- [A Survey on the Explainability of Supervised Machine Learning](https://mlsanity.com/paper/2011.07876/) (2020)
- [Counterfactual Explanations and Algorithmic Recourses for Machine Learning: A Review](https://mlsanity.com/paper/2010.10596/) (2020)
- [Abduction-Based Explanations for Machine Learning Models](https://mlsanity.com/paper/1811.10656/) (2018)
- [Algorithmic recourse under imperfect causal knowledge: a probabilistic approach](https://mlsanity.com/paper/2006.06831/) (2020)
- [On the Tractability of SHAP Explanations](https://mlsanity.com/paper/2009.08634/) (2020)
- [Contrastive Explanation: A Structural-Model Approach](https://mlsanity.com/paper/1811.03163/) (2018)
- [Trustworthy AI](https://mlsanity.com/paper/2011.02272/) (2020)
- [Logic-Based Explainability in Machine Learning](https://mlsanity.com/paper/2211.00541/) (2022)
- [On Computing Probabilistic Explanations for Decision Trees](https://mlsanity.com/paper/2207.12213/) (2022)
- [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)
- [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)
- [Connecting Knowledge Compilation Classes and Width Parameters](https://mlsanity.com/paper/1811.02944/) (2018)
- [Answer-Set Programs for Reasoning about Counterfactual Interventions and Responsibility Scores for Classification](https://mlsanity.com/paper/2107.10159/) (2021)
- [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 Tractability of SHAP-Score-Based Explanations over Deterministic and Decomposable Boolean Circuits](https://mlsanity.com/paper/2007.14045/) (2020)
- [Score-Based Explanations in Data Management and Machine Learning: An Answer-Set Programming Approach to Counterfactual Analysis](https://mlsanity.com/paper/2106.10562/) (2021)

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

- [From Database Repairs to Causality in Databases and Beyond](https://mlsanity.com/paper/2306.09374/) (2023)
- [Attribution-Scores in Data Management and Explainable Machine Learning](https://mlsanity.com/paper/2308.00184/) (2023)

## Abstract (arXiv)

In this expository article we highlight the relevance of explanations for artificial intelligence, in general, and for the newer developments in em explainable AI, referring to origins and connections of and among different approaches. We describe in simple terms, explanations in data management and machine learning that are based on attribution-scores, and counterfactuals as found in the area of causality. We elaborate on the importance of logical reasoning when dealing with counterfactuals, and their use for score computation.