Beyond Correlation: Integrating Causal Inference into Personalization

This research paper introduces the Causal Personalization Framework (CPF), a methodological innovation that integrates causal inference and uplift modeling into modern personalization systems. Traditional recommender and targeting models rely heavily on correlation-based predictions from historical data, which can obscure true cause-and-effect relationships and reinforce bias. The proposed CPF addresses this limitation by embedding structural causal modeling (SCM) and do-calculus into the personalization pipeline to estimate individual treatment effects and optimize for causal lift rather than correlation. The paper unifies theoretical and applied perspectives: it formulates CPF’s causal graph–based design, details algorithmic steps for treatment effect estimation, and demonstrates its practical relevance through case studies in recommendation and retention optimization. Evaluation results, both simulated and applied, show that CPF reduces bias, improves interpretability, and enhances long-term robustness in personalization. This work positions causal reasoning as a foundational layer for responsible AI systems, supporting fairer, more transparent, and outcome-driven personalization across e-commerce, education, and media platforms.