On the Testable Implications of Causal Models with Hidden Variables
On the Testable Implications of Causal Models with Hidden Variables
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发表时间：
2002-08
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通讯作者：
Jin Tian;J. Pearl
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作者：
Jin Tian;J. Pearl
只有当因果模型对支配所生成数据的概率分布施加约束时，其有效性才能得到检验。在存在未测量变量的情况下，因果模型可能会施加两种类型的约束：通过d -分离准则解读的条件独立性，以及尚无通用准则的函数约束。本文提供了一种识别函数约束的系统方法，从而有助于检验因果模型以及从数据中推断此类模型的任务。
The validity of a causal model can be tested only if the model imposes constraints on the probability distribution that governs the generated data. In the presence of unmeasured variables, causal models may impose two types of constraints: conditional independencies, as read through the d-separation criterion, and functional constraints, for which no general criterion is available. This paper offers a systematic way of identifying functional constraints and, thus, facilitates the task of testing causal models as well as inferring such models from data.