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Abstract: Standard Bayesian Optimization (BO) often faces challenges when dealing with high-dimensional and complex optimization problems, frequently failing to pinpoint the most influential parameters efficiently. This work introduces a novel approach, Causal Bayesian Optimization (CBO), leveraging Structural Causal Models (SCMs) to address this limitation. SCMs provide a framework for explicitly representing causal relationships, allowing us to move beyond purely correlational observations and directly target parameters that exert a causal influence on the objective function. We formulate the optimization problem within the SCM framework, enabling a more targeted and potentially faster convergence. The core idea is to utilize the causal structure to guide the exploration of the parameter space, reducing the search burden and improving the identification of critical parameters. This approach offers a significant improvement over traditional BO, especially in scenarios where causal relationships are known or can be reasonably approximated. This paper outlines the theoretical foundations, the methodology for integrating SCMs into BO, and presents a conceptual framework for its implementation, laying the groundwork for future research and practical applications.