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关于因果推断中的一致性假设。

Concerning the consistency assumption in causal inference.

机构信息

Department of Epidemiology, Harvard School of Public Health, Boston, MA 02115, USA.

出版信息

Epidemiology. 2009 Nov;20(6):880-3. doi: 10.1097/EDE.0b013e3181bd5638.

Abstract

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.

摘要

科尔和弗兰加基斯(Epidemiology. 2009;20:3-5)引入了因果推理中一致性假设的符号表示。我扩展了这个符号,并提出了一致性假设的一个改进,明确指出通常给出的一致性陈述实际上是一个假设,而不是公理或定义。这种改进在表明附加假设(这里称为治疗变异无关假设)的重要性时也很有用,这些假设比科尔和弗兰加基斯给出的假设更强,实际上对于阐明可忽略性或可交换性的普通假设是必要的。这种改进还阐明了干预和选择在因果推理中的区别。在讨论非依从性问题时,讨论了可以定义潜在结果的治疗变化范围和可以进行治疗比较的范围之间的区别。随机反事实的使用可以帮助放宽治疗变异无关假设和一致性假设实际上所预设的内容。

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