Diffusion model denoising objectives can smooth optimization landscapes for causal discovery, enabling faster and more stable learning of causal structures in challenging high-dimensional datasets.
This paper proposes DDCD, a new method for discovering causal relationships in data by adapting diffusion model techniques. Instead of using diffusion to generate data, it uses the denoising process to learn causal structures (DAGs) more stably and efficiently than existing methods like NOTEARS, especially when data is high-dimensional or imbalanced.