  Synthetic data can be used in various applications, such as correcting bias
datasets or replacing scarce original data for simulation purposes. Generative
Adversarial Networks (GANs) are considered state-of-the-art for developing
generative models. However, these deep learning models are data-driven, and it
is, thus, difficult to control the generation process. It can, therefore, lead
to the following issues: lack of representativity in the generated data, the
introduction of bias, and the possibility of overfitting the sample's noise.
This article presents the Directed Acyclic Tabular GAN (DATGAN) to address
these limitations by integrating expert knowledge in deep learning models for
synthetic tabular data generation. This approach allows the interactions
between variables to be specified explicitly using a Directed Acyclic Graph
(DAG). The DAG is then converted to a network of modified Long Short-Term
Memory (LSTM) cells to accept multiple inputs. Multiple DATGAN versions are
systematically tested on multiple assessment metrics. We show that the best
versions of the DATGAN outperform state-of-the-art generative models on
multiple case studies. Finally, we show how the DAG can create hypothetical
synthetic datasets.
