This research is focused on generating achievable counterfactual explanations. Given a negative outcome computed by a machine learning model or a decision system, the novel CoGS approach generates (i) a counterfactual solution that represents a positive outcome and (ii) a path that will take us from the negative outcome to the positive one, where each node in the path represents a change in an attribute (feature) value. CoGS computes paths that respect the causal constraints among features. Thus, the counterfactuals computed by CoGS are realistic. CoGS utilizes rule-based machine learning algorithms to model causal dependencies between features. The paper discusses the current status of the research and the preliminary results obtained.
View on arXiv@article{dasgupta2025_2502.09226,
title={ Generating Causally Compliant Counterfactual Explanations using ASP },
author={ Sopam Dasgupta },
journal={arXiv preprint arXiv:2502.09226},
year={ 2025 }
}