论文标题
从小样本中估算大的因果聚to
Estimating large causal polytrees from small samples
论文作者
论文摘要
我们考虑了从相对较小的I.D.估算大量因果多树的问题。样本。这是由于确定因果结构的问题，当变量数量与样本量非常大的情况（例如基因调节网络中）相比非常大。我们给出了一种算法，该算法在此类设置中以很高的精度恢复了树。该算法在基本上没有分布或建模假设下起作用，而不是一些轻度的非分类条件。
We consider the problem of estimating a large causal polytree from a relatively small i.i.d. sample. This is motivated by the problem of determining causal structure when the number of variables is very large compared to the sample size, such as in gene regulatory networks. We give an algorithm that recovers the tree with high accuracy in such settings. The algorithm works under essentially no distributional or modeling assumptions other than some mild non-degeneracy conditions.
