Reviews: Conditional Independence Testing using Generative Adversarial Networks

Originality This paper presents a new way to use GANs in hypothesis testing. It was very interesting to use GANs to construct a null distribution that adapts to the dataset without strong assumptions. The proposed method can be used for feature selection and explainable neural networks. The quantitative experimental results are limited to synthetic data but comprehensive and expected behaviours of the GCIT are observed in synthetic experiments. The only experimental result with real data is shown but it is hard to tell which result is more accurate and powerful.