标题： 线性非高斯无环模型中因果顺序的估计：一种对潜在混杂因素稳健的方法 显示英文标题

标题： Estimation of causal orders in a linear non-Gaussian acyclic model: a method robust against latent confounders

摘要： 我们考虑学习线性非高斯有向无环模型（称为LiNGAM）中变量的因果顺序。已有若干方法在假设所有模型假设均正确的情况下被证明可以一致地估计因果顺序。但是，如果某些假设实际上被违反，则估计结果可能会失真。本文中，我们提出了一种新的算法来学习因果顺序，该算法对模型假设的一种典型违反情况（潜在混杂因子）具有鲁棒性。我们使用人工数据演示了该方法的有效性。 显示英文摘要

摘要： We consider to learn a causal ordering of variables in a linear non-Gaussian acyclic model called LiNGAM. Several existing methods have been shown to consistently estimate a causal ordering assuming that all the model assumptions are correct. But, the estimation results could be distorted if some assumptions actually are violated. In this paper, we propose a new algorithm for learning causal orders that is robust against one typical violation of the model assumptions: latent confounders. We demonstrate the effectiveness of our method using artificial data.