Hierarchical Causal Validation Framework for Explainable Bias Mitigation in LLM-Powered Recommendation Systems

We propose a hierarchical causal validation framework to address bias in LLM-powered recommendation systems while maintaining computational efficiency and explainability. Current methods often treat all causal relationships uniformly, leading to excessive computational overhead or inadequate bias mitigation. The proposed framework stratifies causal edges into high-impact and low-impact tiers based on their bias potential scores, then applies rigorous counterfactual testing and propensity score matching to high-impact edges while employing lightweight conditional independence tests for low-impact edges. A dynamic threshold calibrated via quantile regression ensures adaptive partitioning of the causal graph. The framework integrates seamlessly with conventional recommendation engines by substituting input embeddings with de-biased variants and augments feedback loops with Shapley-based explanations rendered as interactive visualizations. Implemented as a PyTorch Lightning module with a Neural Causal Discovery Layer, the system combines distributed high-impact validation on Ray clusters with ONNX-optimized Transformers for edge deployment. Experimental results demonstrate significant reductions in bias metrics without compromising recommendation quality or latency. Moreover, the hierarchical approach achieves up to 40% faster inference compared to monolithic validation methods while providing auditable causal pathways for regulatory compliance. This work bridges the gap between causal interpretability and scalable deployment in production-grade recommendation systems.