Causal relationships underpin modern science and our ability to reason. Automatically discovering useful causal relationships can greatly accelerate scientific progress and facilitate the creation of machines that can reason like we do. Traditionally, the dominant approaches to causal discovery are statistical, such as the PC algorithm. A new area of research is integrating recent advancement in machine learning with causal discovery. We focus on a series of recent work that leverages new algorithms in deep learning for causal discovery -- notably, generative flow networks (GFlowNets). We discuss the unique perspectives GFlowNets bring to causal discovery.