There is a deep connection between causal discovery and generative models, such as factor analysis, independent component analysis, and various unsupervised deep learning models. Two key concepts that emerge are identifiability and nonstationarity. In this talk, I will review this research, providing some historical perspectives as well as open questions for future research.