  Synthetic data generation becomes prevalent as a solution to privacy leakage
and data shortage. Generative models are designed to generate a realistic
synthetic dataset, which can precisely express the data distribution for the
real dataset. The generative adversarial networks (GAN), which gain great
success in the computer vision fields, are doubtlessly used for synthetic data
generation. Though there are prior works that have demonstrated great progress,
most of them learn the correlations in the data distributions rather than the
true processes in which the datasets are naturally generated. Correlation is
not reliable for it is a statistical technique that only tells linear
dependencies and is easily affected by the dataset's bias. Causality, which
encodes all underlying factors of how the real data be naturally generated, is
more reliable than correlation. In this work, we propose a causal model named
Causal Tabular Generative Neural Network (Causal-TGAN) to generate synthetic
tabular data using the tabular data's causal information. Extensive experiments
on both simulated datasets and real datasets demonstrate the better performance
of our method when given the true causal graph and a comparable performance
when using the estimated causal graph.
