Related papers: Causal discovery of linear non-Gaussian acyclic models in the presence of latent confounders

Causal discovery of linear non-Gaussian acyclic models in the presence of latent confounders

URL: http://arxiv.org/abs/2001.04197v4
Date: Wed, 4 Nov 2020 11:41:54 GMT
Title: Causal discovery of linear non-Gaussian acyclic models in the presence of latent confounders
Authors: Takashi Nicholas Maeda and Shohei Shimizu
Abstract summary: This paper proposes a causal functional model-based method called repetitive causal discovery (RCD) to discover the causal structure of observed variables affected by latent confounders. RCD repeats inferring the causal directions between a small number of observed variables and determines whether the relationships are affected by latent confounders.
Score: 6.1221613913018675
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Causal discovery from data affected by latent confounders is an important and difficult challenge. Causal functional model-based approaches have not been used to present variables whose relationships are affected by latent confounders, while some constraint-based methods can present them. This paper proposes a causal functional model-based method called repetitive causal discovery (RCD) to discover the causal structure of observed variables affected by latent confounders. RCD repeats inferring the causal directions between a small number of observed variables and determines whether the relationships are affected by latent confounders. RCD finally produces a causal graph where a bi-directed arrow indicates the pair of variables that have the same latent confounders, and a directed arrow indicates the causal direction of a pair of variables that are not affected by the same latent confounder. The results of experimental validation using simulated data and real-world data confirmed that RCD is effective in identifying latent confounders and causal directions between observed variables.

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