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 approaches for causal discovery exploit this by performing graph discovery in two steps, first sequentially identifying nodes in reverse order of depth (topological ordering), and secondly pruning the potential relations. This is more efficient since the search is performed over a permutation rather than a graph space. However, existing computational methods for obtaining the Hessian still do not scale as the number of variables and the number of samples are increased. Therefore, inspired by recent innovations in diffusion probabilistic models (DPMs), we propose DiffAN, a topological ordering algorithm that leverages DPMs. Further, we introduce theory for updating the learned Hessian without re-training the neural network, and we show that computing with a subset of samples gives an accurate approximation of the ordering, which allows scaling to datasets with more samples and variables. We show empirically that our method scales exceptionally well to datasets with up to $500$ nodes and up to $10^5$ samples while still performing on par over small datasets with state-of-the-art causal discovery methods. Implementation is available at https://github.com/vios-s/DiffAN .

提出了一种利用扩散概率模型的拓扑序算法(DiffAN)来进行因果发现，可以在提供准确有限子集的情况下更新学习到的海森矩阵，并且得到了更高的可扩展性，可以处理高达500个节点和10^5个样本数据集，并表现出与最先进的因果发现方法相当的性能。

通过拓扑排序进行因果发现的扩散模型