Representation learning with CGAN for casual inference
This addresses a gap in applying CGAN to causal inference, but it appears incremental as it builds on existing adversarial ideas without broad empirical validation.
The paper tackles the problem of representation learning for causal inference by proposing a new method using Conditional Generative Adversarial Nets (CGAN), demonstrating theoretically that a suitable representation function can be found when two distributions are balanced.
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.