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

Shohei Shimizu, Aapo Hyvärinen, Yoshinobu Kawahara, Takashi Washio

Research output: Chapter in Book/Report/Conference proceedingConference contribution

13 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationProceedings of the 25th Conference on Uncertainty in Artificial Intelligence, UAI 2009
PublisherAUAI Press
Pages506-513
Number of pages8
Publication statusPublished - 2009
Externally publishedYes

Publication series

NameProceedings of the 25th Conference on Uncertainty in Artificial Intelligence, UAI 2009

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Applied Mathematics

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