# Reasoning about Counterfactuals and Explanations: Problems, Results and Directions

arXiv: https://arxiv.org/abs/2108.11004 · HTML: https://mlsanity.com/paper/2108.11004/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 32 references that are in the map.
- First posted on arXiv 25 Aug 2021.

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

- [Actionable Recourse in Linear Classification](https://mlsanity.com/paper/1809.06514/) (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)
- [A Modification of the Halpern-Pearl Definition of Causality](https://mlsanity.com/paper/1505.00162/) (2015)
- [Causality-based Explanation of Classification Outcomes](https://mlsanity.com/paper/2003.06868/) (2020)
- [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)
- [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)
- [Answer-Set Programs for Reasoning about Counterfactual Interventions and Responsibility Scores for Classification](https://mlsanity.com/paper/2107.10159/) (2021)
- [Score-Based Explanations in Data Management and Machine Learning](https://mlsanity.com/paper/2007.12799/) (2020)
- [The Tractability of SHAP-Score-Based Explanations over Deterministic and Decomposable Boolean Circuits](https://mlsanity.com/paper/2007.14045/) (2020)
- [Ontological Multidimensional Data Models and Contextual Data Qality](https://mlsanity.com/paper/1704.00115/) (2017)

## Abstract (arXiv)

There are some recent approaches and results about the use of answer-set programming for specifying counterfactual interventions on entities under classification, and reasoning about them. These approaches are flexible and modular in that they allow the seamless addition of domain knowledge. Reasoning is enabled by query answering from the answer-set program. The programs can be used to specify and compute responsibility-based numerical scores as attributive explanations for classification results.