On causal discovery with an equal-variance assumption
On causal discovery with an equal-variance assumption
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DOI：
10.1093/biomet/asz049
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发表时间：
2018-07
期刊：
影响因子：
2.7
通讯作者：
Wenyu Chen;M. Drton;Y Samuel Wang
中科院分区：
文献类型：
--
作者：
Wenyu Chen;M. Drton;Y Samuel Wang
先前的工作表明，当观测数据遵循误差项具有相等方差的结构方程模型时，可以从观测数据中唯一地识别因果结构。我们证明这一事实是由条件方差之间的排序所暗示的。我们证明，对这些方差的排序估计产生了一种简单但最先进的因果结构学习方法，该方法很容易扩展到高维问题。
Prior work has shown that causal structure can be uniquely identified from observational data when these follow a structural equation model whose error terms have equal variance. We show that this fact is implied by an ordering among conditional variances. We demonstrate that ordering estimates of these variances yields a simple yet state-of-the-art method for causal structure learning that is readily extendable to high-dimensional problems.