Score-based Generative Modeling for Conditional Independence Testing

A new method for testing conditional independence (CI) in high-dimensional data is proposed, utilizing score-based generative modeling with sliced conditional score matching and Langevin dynamics, which ensures accurate Type I error control and improved power. Extensive experiments demonstrate that this approach outperforms existing GAN-based methods, offering a more reliable and interpretable solution for CI testing in machine learning and statistics.