Generative adversarial net (GAN)-based tabular data generation has recently received significant attention for its power for data augmentation when available data is limited. Most prior works have applied generic GAN frameworks for tabular data generation without explicitly considering inter-variable relationships, which is important for modeling tabular data distribution. In this work, we design Causal-TGAN, a causally-aware generator architecture that can capture the relationships among variables (continuous-type, discrete-type, and mixed-type) by explicitly modeling the pre-defined inter-variable causal relationships. The flexibility of Causal-TGAN is its capability to support different degrees of subject matter expert domain knowledge (e.g., complete or partial) about the inter-variable causal relations. Extensive experimental results on both simulated and real-world datasets demonstrate that exploiting causal relations in deep generative models could improve the generated tabular data quality compared to the state-of-the-art.