 Award ID(s):
 1835309
 Publication Date:
 NSFPAR ID:
 10280413
 Journal Name:
 Proceedings of the 38th International Conference on Machine Learning
 Volume:
 139
 Page Range or eLocationID:
 1122811239
 Sponsoring Org:
 National Science Foundation
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Learning causal structure from observational data has attracted much attention,and it is notoriously challenging to find the underlying structure in the presenceof confounders (hidden direct common causes of two variables). In this paper,by properly leveraging the nonGaussianity of the data, we propose to estimatethe structure over latent variables with the socalled Triad constraints: we design a form of "pseudoresidual" from three variables, and show that when causal relations are linear and noise terms are nonGaussian, the causal direction between the latent variables for the three observed variables is identifiable by checking a certain kind of independence relationship. In other words, the Triad constraints help us to locate latent confounders and determine the causal direction between them. This goes far beyond the Tetrad constraints and reveals more information about the underlying structure from nonGaussian data. Finally, based on the Triad constraints, we develop a twostep algorithm to learn the causal structure corresponding to measurement models. Experimental results on both synthetic and real data demonstrate the effectiveness and reliability of our method.

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