Traditional marketing uplift models suffer from a fundamental limitation: they typically operate under static assumptions that fail to capture the temporal dynamics of customer responses to marketing interventions. This paper introduces a novel framework that combines causal forest algorithms with deep reinforcement learning to dynamically model marketing uplift effects. Our approach enables the real-time identification of heterogeneous treatment effects across customer segments while simultaneously optimizing intervention strategies through an adaptive learning mechanism. The key innovations of our framework include the following: (1) a counterfactual simulation environment that emulates diverse customer response patterns; (2) an adaptive reward mechanism that captures both immediate and long-term intervention outcomes; and (3) a dynamic policy optimization process that continually refines targeting strategies based on evolving customer behaviors. Empirical evaluations on both simulated and real-world marketing campaign data demonstrate that our approach significantly outperforms traditional static uplift models, achieving up to a 27% improvement in targeting efficiency and an 18% increase in the return on marketing investment. The framework leverages inherent symmetries in customer-intervention interactions, where balanced and symmetric reward structures ensure fair optimization across diverse customer segments. The proposed framework addresses the limitations of existing methods by effectively modeling the dynamic and heterogeneous nature of customer responses to marketing interventions, providing marketers with a powerful tool for implementing personalized and adaptive campaign strategies.