<p>Deep Neural Network Architectural Details.</p>

This paper proposes a counterfactual-guided explainable artificial intelligence framework that integrates deep learning, feature attribution, and counterfactual reasoning to deliver accurate, interpretable, and actionable managerial insights.

The proposed model is evaluated on an employee attrition dataset and a customer churn dataset, as well as in a combined multi-domain setting. Experimental results show that the framework achieves classification accuracies of up to 91.4% for employee attrition prediction and 87.3% for customer churn prediction, with corresponding ROC–AUC scores reaching 0.869 and 0.891, respectively. The model also demonstrates improved probability calibration, reducing the Brier score to 0.128 on the attrition dataset and 0.109 on the churn dataset.