RCD: Repetitive causal discovery of linear non-Gaussian acyclic models with latent confounders
RCD: Repetitive causal discovery of linear non-Gaussian acyclic models with latent confounders
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发表时间：
2020-01
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通讯作者：
Takashi Nicholas Maeda;Shohei Shimizu
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作者：
Takashi Nicholas Maeda;Shohei Shimizu
从受潜在混杂因素影响的数据中发现因果关系是一项重要而艰巨的挑战。基于因果函数模型的方法尚未用于表示关系受潜在混杂因素影响的变量，而一些基于约束的方法可以表示它们。本文提出了一种基于因果函数模型的方法，称为重复因果发现（RCD），用于发现受潜在混杂因素影响的观察变量的因果结构。RCD重复推断少量观察变量之间的因果方向，并确定这些关系是否受到潜在混杂因素的影响。RCD最终生成一个因果图，其中双向箭头表示具有相同潜在混杂因素的一对变量，而有向箭头表示不受相同潜在混杂因素影响的一对变量的因果方向。模拟数据和真实数据的实验验证结果证实，RCD在识别观察变量之间的潜在混杂因素和因果方向方面是有效的。
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 be-tween observed variables.