Toward Trustworthy Churn Prediction: A Comparative Study of Counterfactual Explanation Techniques

Abstract

Customer churn prediction has become a mission-critical application of machine learning across telecommunications, banking, subscription services, and software-as-a-service (SaaS) platforms. Despite significant advances in predictive modeling, the inherent opacity of high-performing black-box classifiers creates substantial barriers to organizational trust, regulatory compliance, and actionable decision support. Counterfactual explanations have emerged as a particularly compelling class of post-hoc interpretability technique, providing stakeholders with algorithmically generated “what-if” scenarios that articulate the minimal feature-level changes necessary to alter a predicted outcome. This paper presents a comprehensive, literature-grounded comparative survey of six prominent counterfactual explanation methodologies applied in the context of customer churn prediction: Wachter et al.’s proximity-constrained optimization, Diverse Counterfactual Explanations (DiCE), the Contrastive Explanations Method (CEM), prototype-based counterfactuals, generative model-based approaches leveraging variational autoencoders, and gradient-based optimization techniques. The evaluation spans seven quality dimensions validity, proximity, sparsity, diversity, plausibility, robustness, and computational efficiency synthesized from the existing literature spanning 2015 to 2025. The paper further situates these methods within the broader discourse on trustworthy AI, fairness, algorithmic recourse, and regulatory compliance, with specific reference to GDPR Article 22 and the EU AI Act. Findings from the surveyed literature consistently indicate that no single counterfactual method achieves universal superiority, and that method selection must be governed by the operational, ethical, and technical priorities of the deployment context. This survey provides researchers and practitioners with a structured analytical foundation for understanding and deploying counterfactual explanations in trustworthy churn prediction systems.