基于高维观测的因果关系分析
Telling cause from effect based on high-dimensional observations
摘要：
我们描述了一种用于推断多维变量之间的线性因果关系的方法。其思想是利用因果分布之间的不对称性，如果原因的协方差阵和将原因映射到结果的结构矩阵都是独立选择的，则会发生这种不对称性。该方法适用于随机和确定性因果关系，前提是维度足够高（在某些实验中，5就足够了）。它适用于高斯和非高斯数据。
英文摘要：
We describe a method for inferring linear causal relations among multi-dimensional variables. The idea is to use an asymmetry between the distributions of cause and effect that occurs if both the covariance matrix of the cause and the structure matrix mapping cause to the effect are independently chosen. The method works for both stochastic and deterministic causal relations, provided that the dimensionality is sufficiently high (in some experiments, 5 was enough). It is applicable to Gaussian as well as non-Gaussian data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
