Neural Comput - ParceLiNGAM: a causal ordering method robust against latent confounders.

Tópicos

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Resumo

We consider learning a causal ordering of variables in a linear nongaussian acyclic model called LiNGAM. Several methods have been shown to consistently estimate a causal ordering assuming that all the model assumptions are correct. But the estimation results could be distorted if some assumptions are violated. In this letter, we propose a new algorithm for learning causal orders that is robust against one typical violation of the model assumptions: latent confounders. The key idea is to detect latent confounders by testing independence between estimated external influences and find subsets (parcels) that include variables unaffected by latent confounders. We demonstrate the effectiveness of our method using artificial data and simulated brain imaging data.

Resumo Limpo

consid learn causal order variabl linear nongaussian acycl model call lingam sever method shown consist estim causal order assum model assumpt correct estim result distort assumpt violat letter propos new algorithm learn causal order robust one typic violat model assumpt latent confound key idea detect latent confound test independ estim extern influenc find subset parcel includ variabl unaffect latent confound demonstr effect method use artifici data simul brain imag data

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