We present a consistent and highly scalable local approach to learn the
causal structure of a linear Gaussian polytree using data from interventional
experiments with known intervention targets. Our methods first learn the
skeleton of the polytree and then orient its edges. The output is a CPDAG
representing the interventional equivalence class of the polytree of the true
underlying distribution. The skeleton and orientation recovery procedures we
use rely on second order statistics and low-dimensional marginal distributions.
We assess the performance of our methods under different scenarios in synthetic
data sets and apply our algorithm to learn a polytree in a gene expression
interventional data set. Our simulation studies demonstrate that our approach
is fast, has good accuracy in terms of structural Hamming distance, and handles
problems with thousands of nodes.

本研究介绍了一种基于干预实验数据的一致且可扩展的本地方法，用于学习线性高斯多叉树的因果结构。该方法首先学习多叉树的骨架，然后定向其边缘，输出为代表真实潜在分布的多叉树的干预等价类的 CPDAG。我们使用二阶统计和低维边缘分布来实现骨架和定向恢复过程。通过对合成数据集的不同情景进行性能评估，并将算法应用于基因表达干预数据集，我们的模拟研究表明我们的方法快速且准确度高，能处理包含数千个节点的问题。