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

2026-07-01

AI Summary

This work addresses the limitation of existing counterfactual explanation methods, which often produce unrealistic or infeasible recommendations due to their failure to explicitly model domain knowledge and intervention constraints. To overcome this, the authors propose the PACE framework, which decouples neural predictive models from symbolic reasoning for the first time. By encoding feasible modification rules—derived from domains such as education and occupation—using Answer Set Programming (ASP), PACE explicitly enforces feasibility during counterfactual generation while preserving immutable attributes. The approach remains model-agnostic yet substantially enhances the plausibility and practical utility of explanations. Experiments on the Adult Income dataset demonstrate that incorporating symbolic constraints effectively balances counterfactual validity with domain feasibility.

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.

Problem

• counterfactual explanations
• feasibility
• domain knowledge
• intervention constraints
• explainable AI

Innovation

• neuro-symbolic AI
• counterfactual explanations
• feasibility-aware
• symbolic reasoning
• model-agnostic