PACE: A Neuro-Symbolic Framework for Plausible and Actionable Counterfactual Explanations
PACE is a modular neuro-symbolic framework that generates feasibility-aware counterfactual explanations by combining neural predictive models with symbolic reasoning. It enforces domain-specific constraints to produce realistic and actionable recommendations, as demonstrated on the Adult Income dataset.
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[Submitted on 1 Jul 2026]
Title:PACE: A Neuro-Symbolic Framework for Plausible and Actionable Counterfactual Explanations
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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.
Subjects:
Artificial Intelligence (cs.AI)
Cite as: arXiv:2607.01306 [cs.AI]
(or arXiv:2607.01306v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2607.01306
arXiv-issued DOI via DataCite
Submission history
From: Fadi Al Machot [view email] [v1] Wed, 1 Jul 2026 16:55:06 UTC (157 KB)
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