Title: Deep Causal Generative Modeling for Tabular Data Imputation and Intervention. Abstract: Tabular data synthesis could overcome the tabular data incompleteness and data availability issue. In most prior works, deep generative models are basically constructed following standard architecture designs. However, these works do not consider the inter-relationships among the features, or the latent variables. To fully leverage these inter-relationships, we develop a novel causal-aware asymmetric variational autoencoder architecture (CAT) for tabular data generation, imputation, and intervention. The developed model, called CAT-MIWAE, learns exogenous causal representation with a pre-defined causal graph in incomplete data context. It provides interpretability for partially observed features and could efficiently address missing value imputation problem. Besides, CAT-MIWAE can sample data from distributions under arbitrary conditions and interventions. This merit enables us to actively generate counterfactuals or debiased fair data samples for any subpopulation of interest. To validate the effectiveness of the proposed causally aware models, we conduct extensive experiments on real-world tabular datasets. Experiments show that the proposed models outperform the state of the art models. Moreover, we perform CATE estimations to show that CAT-MIWAE model could appropriately extrapolate any conditional or interventional distributions from the original observed data distribution.