  Conditionality has become a core component for Generative Adversarial
Networks (GANs) for generating synthetic images. GANs are usually using latent
conditionality to control the generation process. However, tabular data only
contains manifest variables. Thus, latent conditionality either restricts the
generated data or does not produce sufficiently good results. Therefore, we
propose a new methodology to include conditionality in tabular GANs inspired by
image completion methods. This article presents ciDATGAN, an evolution of the
Directed Acyclic Tabular GAN (DATGAN) that has already been shown to outperform
state-of-the-art tabular GAN models. First, we show that the addition of
conditional inputs does hinder the model's performance compared to its
predecessor. Then, we demonstrate that ciDATGAN can be used to unbias datasets
with the help of well-chosen conditional inputs. Finally, it shows that
ciDATGAN can learn the logic behind the data and, thus, be used to complete
large synthetic datasets using data from a smaller feeder dataset.
