TY - GEN

T1 - A direct method for estimating a causal ordering in a linear non-Gaussian acyclic model

AU - Shimizu, Shohei

AU - Hyvärinen, Aapo

AU - Kawahara, Yoshinobu

AU - Washio, Takashi

N1 - Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.

PY - 2009

Y1 - 2009

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=80053162211&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=80053162211&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:80053162211

T3 - Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence, UAI 2009

SP - 506

EP - 513

BT - Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence, UAI 2009

PB - AUAI Press

ER -