Neural Comput - Causality in linear nongaussian acyclic models in the presence of latent gaussian confounders.

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Resumo

LiNGAM has been successfully applied to some real-world causal discovery problems. Nevertheless, causal sufficiency is assumed; that is, there is no latent confounder of the observations, which may be unrealistic for real-world problems. Taking into the consideration latent confounders will improve the reliability and accuracy of estimations of the real causal structures. In this letter, we investigate a model called linear nongaussian acyclic models in the presence of latent gaussian confounders (LiNGAM-GC) which can be seen as a specific case of lvLiNGAM. This model includes the latent confounders, which are assumed to be independent gaussian distributed and statistically independent of the disturbances. To tackle the causal discovery problem of this model, first we propose a pairwise cumulant-based measure of causal directions for cause-effect pairs. We prove that in spite of the presence of latent gaussian confounders, the causal direction of the observed cause-effect pair can be identified under the mild condition that the disturbances are simultaneously supergaussian or subgaussian. We propose a simple and efficient method to detect the violation of this condition. We extend our work to multivariate causal network discovery problems. Specifically we propose algorithms to estimate the causal network structure, including causal ordering and causal strengths, using an iterative root finding-removing scheme based on pairwise measure. To address the redundant edge problem due to the finite sample size effect, we develop an efficient bootstrapping-based pruning algorithm. Experiments on synthetic data and real-world data have been conducted to show the applicability of our model and the effectiveness of our proposed algorithms.

Resumo Limpo

lingam success appli realworld causal discoveri problem nevertheless causal suffici assum latent confound observ may unrealist realworld problem take consider latent confound will improv reliabl accuraci estim real causal structur letter investig model call linear nongaussian acycl model presenc latent gaussian confound lingamgc can seen specif case lvlingam model includ latent confound assum independ gaussian distribut statist independ disturb tackl causal discoveri problem model first propos pairwis cumulantbas measur causal direct causeeffect pair prove spite presenc latent gaussian confound causal direct observ causeeffect pair can identifi mild condit disturb simultan supergaussian subgaussian propos simpl effici method detect violat condit extend work multivari causal network discoveri problem specif propos algorithm estim causal network structur includ causal order causal strength use iter root findingremov scheme base pairwis measur address redund edg problem due finit sampl size effect develop effici bootstrappingbas prune algorithm experi synthet data realworld data conduct show applic model effect propos algorithm

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