Reasoning about Counterfactuals and Explanations: Problems, Results and Directions
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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.
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References · 13
- 2018Actionable Recourse in Linear Classificationcited by 328
- 2020Algorithmic recourse under imperfect causal knowledge: a probabilistic approachcited by 140
- 2020On the Tractability of SHAP Explanationscited by 92
- 2015A Modification of the Halpern-Pearl Definition of Causalitycited by 47
- 2020Causality-based Explanation of Classification Outcomescited by 25
- 2020Declarative Approaches to Counterfactual Explanations for Classificationcited by 14
- 2014From Causes for Database Queries to Repairs and Model-Based Diagnosis and Backcited by 13
- 2016Quantifying Causal Effects on Query Answering in Databasescited by 10
- 2020An ASP-Based Approach to Counterfactual Explanations for Classificationcited by 10
- 2021Answer-Set Programs for Reasoning about Counterfactual Interventions and Responsibility Scores for Classificationcited by 9
- 2020Score-Based Explanations in Data Management and Machine Learningcited by 4
- 2020The Tractability of SHAP-Score-Based Explanations over Deterministic and Decomposable Boolean Circuitscited by 4
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