Discovery of Exogenous Variables in Data with More Variables Than Observations-猫眼课题宝

DOI：

10.1007/978-3-642-15819-3_10

发表时间：

2009-04

期刊：

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其他

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作者：

Yasuhiro Sogawa;Shohei Shimizu;Aapo Hyvärinen;T. Washio;Teppei Shimamura;S. Imoto

已经提出了许多统计方法来估计经典情况下的因果模型，其变量少于观察值。然而，包括基因表达数据在内的现代数据集增加了在变量比观测值多几个数量级的挑战性情况下对高维因果建模的需求。在本文中，我们提出了一种在线性非高斯因果模型中查找外生变量的方法，该方法需要比传统方法小得多的样本量，并且即使在变量比观测值多几个数量级的情况下也能发挥作用。外生变量作为激活模型中因果链的触发器，识别它们可以提高实验设计的效率并更好地理解因果机制。我们用人工数据和真实世界的基因表达数据进行实验来评估该方法。

Many statistical methods have been proposed to estimate causal models in classical situations with fewer variables than observations. However, modern datasets including gene expression data increase the needs of high-dimensional causal modeling in challenging situations with orders of magnitude more variables than observations. In this paper, we propose a method to find exogenous variables in a linear non-Gaussian causal model, which requires much smaller sample sizes than conventional methods and works even when orders of magnitude more variables than observations. Exogenous variables work as triggers that activate causal chains in the model, and their identification leads to more efficient experimental designs and better understanding of the causal mechanism. We present experiments with artificial data and real-world gene expression data to evaluate the method.

Discovery of Exogenous Variables in Data with More Variables Than Observations

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