  Conditional Generative Adversarial Nets (CGAN) is often used to improve
conditional image generation performance. However, there is little research on
Representation learning with CGAN for causal inference. This paper proposes a
new method for finding representation learning functions by adopting the
adversarial idea. We apply the pattern of CGAN and theoretically emonstrate the
feasibility of finding a suitable representation function in the context of two
distributions being balanced. The theoretical result shows that when two
distributions are balanced, the ideal representation function can be found and
thus can be used to further research.
