  We propose a generative Causal Adversarial Network (CAN) for learning and
sampling from conditional and interventional distributions. In contrast to the
existing CausalGAN which requires the causal graph to be given, our proposed
framework learns the causal relations from the data and generates samples
accordingly. The proposed CAN comprises a two-fold process namely Label
Generation Network (LGN) and Conditional Image Generation Network (CIGN). The
LGN is a GAN-based architecture which learns and samples from the causal model
over labels. The sampled labels are then fed to CIGN, a conditional GAN
architecture, which learns the relationships amongst labels and pixels and
pixels themselves and generates samples based on them. This framework is
equipped with an intervention mechanism which enables. the model to generate
samples from interventional distributions. We quantitatively and qualitatively
assess the performance of CAN and empirically show that our model is able to
generate both interventional and conditional samples without having access to
the causal graph for the application of face generation on CelebA data.
