Answer-Set Programs for Repair Updates and Counterfactual Interventions
This work provides a framework for addressing data consistency and causality issues in databases and machine learning, but it appears incremental as it builds on existing answer-set programming concepts.
The paper describes answer-set programs with annotations for specifying database repairs, consistent query answering, secrecy views, and counterfactual interventions in databases and machine learning, using simple examples to illustrate these applications.
We briefly describe -- mainly through very simple examples -- different kinds of answer-set programs with annotations that have been proposed for specifying: database repairs and consistent query answering; secrecy view and query evaluation with them; counterfactual interventions for causality in databases; and counterfactual-based explanations in machine learning.