A direct method for estimating a causal ordering in a linear non-Gaussian acyclic model

Abstract

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 datagenerating process of variables. Recently, it was shown that use of non-Gaussianity identifies a causal ordering of variables in a linear acyclic model 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 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.

Appears in Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence (UAI2009)

Cited by in corpus (6)

• GroupLiNGAM: Linear non-Gaussian acyclic models for sets of variables
• A review of some recent advances in causal inference
• Learning LiNGAM based on data with more variables than observations
• Causal Discovery in a Binary Exclusive-or Skew Acyclic Model: BExSAM
• The Effect of Noise Level on Causal Identification with Additive Noise Models
• Causal Identification with Additive Noise Models: Quantifying the Effect of Noise