Dormant Independence
Dormant Independence
复制标题
休眠独立
DOI：
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发表时间：
2008
期刊：
影响因子：
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通讯作者：
J. Pearl
中科院分区：
文献类型：
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作者：
I. Shpitser;J. Pearl
从非实验数据构建因果图取决于图结构对与图兼容的所有概率分布施加的一组约束。这些约束有两种类型：条件独立性和代数约束，最早由 Verma 指出。虽然条件独立性已得到充分研究并经常用于因果归纳算法，但 Verma 约束仍然知之甚少，并且很少应用。在本文中，我们研究了 Verma 约束的一个特殊子集，它易于理解、易于识别且易于应用；它们源于“休眠的独立性”，即干预分布中存在的条件独立性。我们给出了一个完整的算法，用于确定因果图是否蕴涵两组变量之间的潜在独立性，使得这种独立性是可识别的，换句话说，它是否存在于无需诉诸干预即可预测的干预分布中。我们进一步展示了休眠独立性在模型测试和归纳中的有用性，通过给出一种算法，该算法使用休眠独立性所带来的约束来从给定的因果图中修剪无关的边缘。
The construction of causal graphs from nonexperimental data rests on a set of constraints that the graph structure imposes on all probability distributions compatible with the graph. These constraints are of two types: conditional independencies and algebraic constraints, first noted by Verma. While conditional independencies are well studied and frequently used in causal induction algorithms, Verma constraints are still poorly understood, and rarely applied. In this paper we examine a special subset of Verma constraints which are easy to understand, easy to identify and easy to apply; they arise from "dormant independencies," namely, conditional independencies that hold in interventional distributions. We give a complete algorithm for determining if a dormant independence between two sets of variables is entailed by the causal graph, such that this independence is identifiable, in other words if it resides in an interventional distribution that can be predicted without resorting to interventions. We further show the usefulness of dormant independencies in model testing and induction by giving an algorithm that uses constraints entailed by dormant independencies to prune extraneous edges from a given causal graph.