  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.

Machine Learning