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arXiv · 2502.09226

Generating Causally Compliant Counterfactual Explanations using ASP

Sopam Dasgupta
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Abstract

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.

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Sopam Dasgupta. 2025-02-13. Generating Causally Compliant Counterfactual Explanations using ASP. https://doi.org/10.4204/eptcs.416.30

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