论文标题
在潜在混杂因素的存在下，线性非高斯无环模型的因果发现
Causal discovery of linear non-Gaussian acyclic models in the presence of latent confounders
论文作者
论文摘要
从潜在混杂因素影响的数据中发现的因果发现是一个重要且困难的挑战。基于因果功能模型的方法尚未用于呈现其关系受潜在混杂因素影响的变量，而某些基于约束的方法可以呈现它们。本文提出了一种基于因果功能模型的方法，称为重复因果发现（RCD），以发现受潜在混杂因素影响的观察到的变量的因果结构。 RCD重复推断少数观察到的变量之间的因果方向，并确定关系是否受到潜在混杂因素的影响。 RCD最终产生了一个因果图，其中双向箭头指示具有相同潜在混杂因子的一对变量，而有向箭头表示一对不受同一潜伏混杂因子影响的一对变量的因果方向。使用模拟数据和现实世界数据实验验证的结果证实，RCD有效地识别观察到的变量之间的潜在混杂因素和因果方向。
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.
