A general identification condition for causal effects
A general identification condition for causal effects
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DOI：
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发表时间：
2002-07
影响因子：
1.5
通讯作者：
Jin Tian;J. Pearl
中科院分区：
文献类型：
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作者：
Jin Tian;J. Pearl
本文涉及以下组合对行动或政策干预措施的影响：（i）非实验性数据和（ii）实质性假设。假设以有向无环图的形式编码，也称为“因果图”，其中某些变量被认为是未观察到的。本文为单胎变量对模型中所有其他变量的因果效应的识别性建立了必要的标准，并为单胎变量对任何一组变量的影响提供了强大的足够标准。
This paper concerns the assessment of the effects of actions or policy interventions from a combination of: (i) nonexperimental data, and (ii) substantive assumptions. The assumptions are encoded in the form of a directed acyclic graph, also called "causal graph", in which some variables are presumed to be unobserved. The paper establishes a necessary and sufficient criterion for the identifiability of the causal effects of a singleton variable on all other variables in the model, and a powerful sufficient criterion for the effects of a singleton variable on any set of variables.