Causal global optimization (CGO) aims to complete optimization tasks through causal inference. In the high-dimensional CGO problems, traditional causal Bayesian optimization (CBO) methods struggle with the curse of dimensionality attributed to the number of variables in the causal graph, and scale inconsistency among Gaussian Process (GP) models. These issues limit the application of CBO in domains requiring optimization over large causal graphs. To address these limitations, this paper proposes a high-dimensional causal Bayesian optimization (HCBO) algorithm. To address the curse of dimensionality, HCBO introduces a submodularity indicator for variable subsets through the concept of causal intrinsic dimensionality (CID). It then uses the submodular optimization algorithm to find approximations of CID within polynomial sample complexity. Theoretically, we disclose a sufficient condition for CID’s existence. To address the issue of scale inconsistency among GP models, HCBO introduces a scale-normalized scoring function, ensuring stable identification of the optimal GP model corresponding to CID for intervention. Extensive experiments are conducted on high-dimensional synthetic and real-world tasks, i.e., coral ecology and health. The existence of CID is verified across the datasets of all tasks. HCBO achieves state-of-the-art performance in CGO problems and can handle causal graphs at a scale 10 times larger than that manageable by previous CBO methods.