Missing Value Imputation (MVI) is a critical challenge in data science and machine learning, particularly in high-dimensional tabular data. Existing methods exhibit limitations in capturing complex relationships between variables, especially in effectively utilizing latent causal structures. This study proposes a causal discovery and attention-based MVI method (Causal Attention-based Missing value Imputation, CAMI), which introduces causal discovery techniques to generate causal graphs that guide attention allocation. By combining multi-head self-attention, it dynamically captures the relationships between variables, thereby improving imputation performance. Experimental results show that CAMI outperforms traditional and deep learning-based imputation methods across multiple real-world datasets and varying missing ratios, demonstrating stability and robustness, especially in data with high missing proportions. This method not merely increases imputation accuracy but also enhances data relationship interpretability, offering new insights for MVI tasks in tabular datasets.