Structural equation models and Bayesian networks have been widely used to
analyze causal relations between continuous variables. In such frameworks,
linear acyclic models are typically used to model the data-generating process
of variables. Recently, it was shown that use of non-Gaussianity identifies the
full structure of a linear acyclic model, i.e., a causal ordering of variables
and their connection strengths, without using any prior knowledge on the
network structure, which is not the case with conventional methods. However,
existing estimation methods are based on iterative search algorithms and may
not converge to a correct solution in a finite number of steps. In this paper,
we propose a new direct method to estimate a causal ordering and connection
strengths based on non-Gaussianity.
In contrast to the previous methods, our algorithm requires no algorithmic
parameters and is guaranteed to converge to the right solution within a small
fixed number of steps if the data strictly follows the model.

本文介绍了一种基于非高斯性的新的、无需迭代算法参数即可估计因果排序和连接强度的方法，该方法能够在少量的步骤内保证求解正确，能够处理连续变量之间的因果关系。