The Counterfactual–Dialectical Optimization Framework: A Prescriptive Approach to Employee Attrition Management with Empirical Validation

Abstract

Employee attrition represents a significant burden, yet predictive models often fail to provide actionable retention strategies, creating a critical prediction–prescription gap. This paper introduces the Counterfactual–Dialectical Optimisation (CDO) framework, a novel methodology that bridges this gap by integrating predictive modeling, robust causal inference, and budget-constrained optimization. We first illustrate the framework’s mechanics on the synthetic Human Resources (HR) dataset, then conduct a rigorous proof-of-concept on the empirical ‘Saudi Employee Attrition Dataset’ to test its real-world applicability. In our empirical validation, we employ Propensity Score Matching (PSM) to estimate the causal effects of interventions while controlling for confounding variables. The results on the real-world dataset show that while predicting attrition is challenging Area Under the Curve (AUC ≈ 0.60), the framework successfully identified a deserved promotion as a powerful retention lever, causally reducing attrition probability by an estimated 23.9 percentage points. Acting on this insight, the optimization layer efficiently allocated the entire budget to this single, high-impact strategy for high-priority employees. This work provides a validated blueprint for shifting from passive prediction to active, Return on Investment (ROI)-driven prescription in strategic workforce management, demonstrating how to derive clear, actionable guidance even from complex, real-world data.