Faculty of Data Science, Shiga University, Hikone, Japan.
The RIKEN Center for Advanced Intelligence Project, Tokyo, Japan.
Prev Sci. 2019 Apr;20(3):431-441. doi: 10.1007/s11121-018-0901-x.
Causal structure learning is one of the most exciting new topics in the fields of machine learning and statistics. In many empirical sciences including prevention science, the causal mechanisms underlying various phenomena need to be studied. Nevertheless, in many cases, classical methods for causal structure learning are not capable of estimating the causal structure of variables. This is because it explicitly or implicitly assumes Gaussianity of data and typically utilizes only the covariance structure. In many applications, however, non-Gaussian data are often obtained, which means that more information may be contained in the data distribution than the covariance matrix is capable of containing. Thus, many new methods have recently been proposed for using the non-Gaussian structure of data and inferring the causal structure of variables. This paper introduces prevention scientists to such causal structure learning methods, particularly those based on the linear, non-Gaussian, acyclic model known as LiNGAM. These non-Gaussian data analysis tools can fully estimate the underlying causal structures of variables under assumptions even in the presence of unobserved common causes. This feature is in contrast to other approaches. A simulated example is also provided.
因果结构学习是机器学习和统计学领域中最令人兴奋的新主题之一。在包括预防科学在内的许多实证科学中，都需要研究各种现象背后的因果机制。然而，在许多情况下，经典的因果结构学习方法无法估计变量的因果结构。这是因为它明确或隐含地假设数据的正态性，并且通常仅利用协方差结构。然而，在许多应用中，通常会获得非正态数据，这意味着数据分布中可能包含比协方差矩阵所能包含的更多信息。因此，最近提出了许多新的方法来利用数据的非正态结构并推断变量的因果结构。本文向预防科学家介绍了这种因果结构学习方法，特别是基于线性、非正态、无环模型（称为 LiNGAM）的方法。这些非正态数据分析工具可以在假设条件下，甚至在存在未观测到的共同原因的情况下，充分估计变量的潜在因果结构。这一特点与其他方法形成对比。还提供了一个模拟示例。
