Generating Causally Compliant Counterfactual Explanations using ASP

• URL: http://arxiv.org/abs/2502.09226v1
• Date: Thu, 13 Feb 2025 11:51:53 GMT
• Title: Generating Causally Compliant Counterfactual Explanations using ASP
• Authors: Sopam Dasgupta,
• Abstract summary: CoGS approach generates a counterfactual solution that represents a positive outcome. CoGS computes paths that respect the causal constraints among features.
• Score: 0.0
• License:
• 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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