PACE-WF: Privacy-Preserving Counterfactual Evaluation Framework for Employee Lifecycle Decision Intelligence in Cloud HR Platforms

Abstract

Cloud HR platforms increasingly shape employee lifecycle decisions through interconnected processes such as workforce planning, hiring, onboarding, performance evaluation, compensation review, learning assignment, internal mobility, and retention management. Although these platforms provide extensive reporting and predictive analytics capabilities, most organizations still assess the consequences of workforce policy changes after implementation rather than before deployment. This creates a critical decision gap because changes in merit guidelines, mobility eligibility, performance calibration, learning recommendations, or retention interventions may produce unintended effects on workforce equity, employee movement, cost exposure, manager workload, and audit readiness. This paper proposes PACE-WF, a privacy-preserving counterfactual workforce policy simulation framework designed to evaluate employee lifecycle decisions in cloud HR platforms before they are applied to real employees. The framework combines synthetic workforce data generation, employee lifecycle event modeling, interpretable predictive analytics, counterfactual policy testing, fairness assessment, and audit-oriented decision evidence. Using a SAP SuccessFactors-inspired synthetic workforce environment, the study models cross-functional relationships among employee profiles, job and position records, performance ratings, compensation bands, learning histories, internal applications, promotions, transfers, and attrition outcomes. The proposed approach is evaluated against traditional rule-based reporting and predictive baseline models across five dimensions: prediction accuracy, policy simulation reliability, fairness and equity impact, privacy-utility preservation, and operational efficiency. The experimental findings show that PACE-WF improves cross-domain workforce outcome estimation, reduces simulated privacy exposure while preserving analytical utility, identifies high-risk policy effects before deployment, and strengthens fairness review across retention, mobility, pay equity, learning access, and promotion-related outcomes. By shifting cloud HR analytics from retrospective reporting to counterfactual policy intelligence, PACE-WF offers a practical foundation for responsible employee lifecycle decision support in privacy-sensitive enterprise environments.