PACE: A Neuro-Symbolic Framework for Plausible and Actionable Counterfactual Explanations

• URL: http://arxiv.org/abs/2607.01306v1
• Date: Wed, 01 Jul 2026 16:55:06 GMT
• Title: PACE: A Neuro-Symbolic Framework for Plausible and Actionable Counterfactual Explanations
• Abstract summary: PACE is a modular neuro-symbolic framework for generating feasibility-aware counterfactual explanations.<n>By explicitly modeling feasible interventions, the framework produces explanations consistent with domain knowledge.<n>Results highlight the trade-off between counterfactual validity and plausibility.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Counterfactual explanations explain machine learning predictions by identifying minimal input changes that would alter a model's decision. Although many existing methods successfully generate prediction-changing alternatives, they often produce unrealistic or infeasible recommendations due to a lack of explicit mechanisms for incorporating domain knowledge and intervention constraints. Neuro-symbolic AI offers a promising direction by combining data-driven predictive models with symbolic reasoning capable of representing human-understandable rules and feasible actions. This paper presents PACE, a modular neuro-symbolic framework for generating feasibility-aware counterfactual explanations. The framework separates prediction and reasoning into two components: a neural predictive model for classification and a symbolic reasoning layer that enforces domain-specific constraints during counterfactual generation. By explicitly modeling feasible interventions, the framework produces explanations consistent with domain knowledge while remaining interpretable and actionable. The approach is model-agnostic and adaptable to domains requiring realistic decision support. A case study is conducted on the Adult Income dataset, combining a multilayer perceptron classifier with Answer Set Programming (ASP) rules encoding feasible modifications to education, occupation, and working hours while preserving immutable attributes. Results highlight the trade-off between counterfactual validity and plausibility and show that symbolic constraints yield explanations that better satisfy domain-specific feasibility requirements, illustrating the potential of neuro-symbolic methods for transparent, feasibility-aware counterfactual explanation in explainable AI.

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