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

Hierarchical Causal Validation Framework for Explainable Bias Mitigation in LLM-Powered Recommendation Systems | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Hierarchical Causal Validation Framework for Explainable Bias Mitigation in LLM-Powered Recommendation Systems xiaochen xiao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6496419/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract 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. Physical sciences/Mathematics and computing/Computational science Physical sciences/Mathematics and computing/Computer science Physical sciences/Mathematics and computing/Information technology Physical sciences/Mathematics and computing/Scientific data Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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