Reasoning about Counterfactuals and Explanations: Problems, Results and Directions

TL;DR

This paper reviews recent answer-set programming methods for modeling counterfactual interventions and explanations in classification, highlighting their flexibility, modularity, and ability to generate responsibility-based scores.

Contribution

It discusses recent advances in using answer-set programming for counterfactual reasoning and explanations, emphasizing their integration with domain knowledge and scoring mechanisms.

Findings

Answer-set programming effectively models counterfactual interventions.

The approach allows seamless addition of domain knowledge.

Responsibility-based scores provide attributive explanations.

Abstract

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.