Learning Causal Structures Based on Markov Equivalence Class
Learning Causal Structures Based on Markov Equivalence Class
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DOI：
10.1007/11564089_9
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发表时间：
2005-10
期刊：
影响因子：
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通讯作者：
Y. He;Z. Geng;Xun Liang
中科院分区：
文献类型：
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作者：
Y. He;Z. Geng;Xun Liang
由于从观测数据的因果学习不能避免具有相同的马尔可夫性质的因果结构的固有的不确定性，本文讨论了在马尔可夫等价类的因果结构学习。我们提出，一个给定的变量和它的相邻变量的额外的因果信息，如专家的知识或随机化实验的数据，可以细化马尔可夫等价类到一些更小的约束等价子类，每个子类可以表示为一个链图。这些子类的顺序刻画提供了一种学习因果结构的方法。根据该方法，可以用随机化实验的数据对等价类进行迭代划分，直到确定确切的因果结构。
Because causal learning from observational data cannot avoid the inherent indistinguishability for causal structures that have the same Markov properties, this paper discusses causal structure learning within a Markov equivalence class. We present that the additional causal information about a given variable and its adjacent variables, such as knowledge from experts or data from randomization experiments, can refine the Markov equivalence class into some smaller constrained equivalent subclasses, and each of which can be represented by a chain graph. Those sequential characterizations of subclasses provide an approach for learning causal structures. According to the approach, an iterative partition of the equivalent class can be made with data from randomization experiments until the exact causal structure is identified.