Hierarchical Causal Validation Framework for Fair and Explainable LLM-Powered Recommendations with Reduced Computational Overhead

Abstract

We propose a Hierarchical Causal Validation Framework (HCVF) to address bias mitigation in LLM-powered recommendation systems while maintaining explainability and computational efficiency. The framework introduces a dynamic stratification mechanism to identify high-impact causal relationships, which are then validated through tiered hybrid methods combining hierarchical Bayesian propensity scoring and attention-based conditional independence tests. The proposed method quantifies bias contributions using Shapley values over the causal graph, enabling transparent attribution of fairness violations to specific model components. Furthermore, mitigationaware embedding adjustments are applied selectively to highbias pathways, reducing unnecessary computational overhead compared to global debiasing approaches. A neural causal discovery layer continuously refines the underlying causal structure, ensuring adaptability to evolving data distributions. Experiments on real-world recommendation tasks demonstrate that HCVF achieves superior bias reduction while preserving recommendation accuracy, with a 40% reduction in validation costs relative to state-of-the-art baselines. The framework’s hierarchical design provides interpretable insights into bias propagation mechanisms, making it particularly valuable for high-stakes applications where fairness and transparency are critical. This work bridges the gap between causal fairness theory and practical deployment constraints in large-scale recommendation systems.