# Diffusion Models for Causal Discovery via Topological Ordering

@article{Sanchez2022DiffusionMF, title={Diffusion Models for Causal Discovery via Topological Ordering}, author={Pedro Sanchez and Xiao Liu and Alison Q. O'Neil and Sotirios A. Tsaftaris}, journal={ArXiv}, year={2022}, volume={abs/2210.06201} }

Discovering causal relations from observational data becomes possible with additional assumptions such as considering the functional relations to be constrained as nonlinear with additive noise. In this case, the Hessian of the data log-likelihood can be used for ﬁnding leaf nodes in a causal graph. Topological ordering ap-proaches for causal discovery exploit this by performing graph discovery in two steps, ﬁrst sequentially identifying nodes in reverse order of depth ( topological ordering…

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