Concerning the Consistency Assumption in Causal Inference
Concerning the Consistency Assumption in Causal Inference
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DOI：
10.1097/ede.0b013e3181bd5638
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发表时间：
2009-11-01
期刊：
影响因子：
5.4
通讯作者：
VanderWeele, Tyler J.
中科院分区：
文献类型：
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作者：
VanderWeele, Tyler J.
科尔和弗兰加基斯（流行病学。2009;20：3-5）引入了因果推理中一致性假设的符号。我扩展了这个符号，并提出了一个改进的一致性假设，明确的一致性声明，通常给出，实际上是一个假设，而不是公理或定义。这种改进也有助于表明，比科尔和弗兰加基斯给出的假设更强的额外假设（这里称为治疗变异无关性假设），实际上在阐明可互换性或可交换性的普通假设时是必要的。这种改进进一步阐明了在因果关系推理中干预和选择之间的区别。治疗变化的范围之间的区别，可以定义的潜在结果和治疗比较的范围进行了讨论，有关的问题不遵守。使用随机反事实可以帮助放松治疗变异无关性假设和一致性假设的有效前提。
Cole and Frangakis (Epidemiology. 2009;20:3-5) introduced notation for the consistency assumption in causal inference. I extend this notation and propose a refinement of the consistency assumption that makes clear that the consistency statement, as ordinarily given, is in fact an assumption and not an axiom or definition. The refinement is also useful in showing that additional assumptions (referred to here as treatment-variation irrelevance assumptions), stronger than those given by Cole and Frangakis, are in fact necessary in articulating the ordinary assumptions of ignorability or exchangeability. The refinement furthermore sheds light on the distinction between intervention and choice in reasoning about causality. A distinction between the range of treatment variations for which potential outcomes can be defined and the range for which treatment comparisons are made is discussed in relation to issues of nonadherence. The use of stochastic counterfactuals can help relax what is effectively being presupposed by the treatment-variation irrelevance assumption and the consistency assumption.