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Identifying the Causal Structure from the Correlation Matrix

机译：识别来自相关矩阵的因果结构

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摘要

The structure with the best posterior probability as calculated by the Cooper and Herskovits formula does not necessarily give the correct causal structure. There is a need to understand why the posterior probability structure sometimes gives incorrect structures. A new norm is identifiedthat simplifies identification of the causal structure. In this research some theorems are proved to identify the causal structure. The aim of the research on which this article reports, was to place the first two theorems that this author proved about the norm, that gives an alternative tothe Cooper and Herskovits algorithm, in the public domain. There are a number of theorems still not proven and other researchers can give valuable input if they follow the same approach. For a tree structure the best posterior probability structure gives the correct causal structure, howeverwhen v-structures are present there is often a discrepancy between the actual causal structure and the highest posterior probability structure. This article helps to clarify this situation and recommends an approach that should eventually lead to a better understanding of causal processes.
机译：由Cooper和Herskovits公式计算的具有最佳后验概率的结构并不一定提供正确的因果结构。需要了解后概率结构有时会产生不正确的结构。确定了新的标准，简化了因果结构的识别。在本研究中，证明了一些定理以识别因果结构。本文报告的研究的目的是将本作者证明的前两个定理放置在公共领域中提供替代的Cooper和Herskovits算法。有许多定理仍然没有被证明，其他研究人员可以提供有价值的投入，如果他们遵循相同的方法。对于树结构，最好的后验概率结构具有正确的因果结构，然而，当V结构存在时，实际的因果结构和最高后概率结构之间通常存在差异。本文有助于澄清这种情况，并推荐一种最终应更好地了解因果流程的方法。

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