A new criterion for confounder selection.

A new criterion for confounder selection.
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DOI:
10.1111/j.1541-0420.2011.01619.x
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发表时间:
2011-12
期刊:
影响因子:
1.9
通讯作者:
Shpitser I
Shpitser I
中科院分区:
数学3区
文献类型:
--
作者:
VanderWeele TJ;Shpitser I

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我们提出了一个新的标准混杂选择时,潜在的因果结构是未知的,只有有限的知识。我们假设所有考虑的协变量都是治疗前变量,并且对于每个协变量,已知(i)协变量是否是治疗的原因,以及(ii)协变量是否是结局的原因。假设协变量之间的因果关系未知。我们建议控制任何协变量,无论是治疗的原因或结果或两者兼而有之。我们表明,无论实际的潜在因果结构,如果观察到的协变量的任何子集足以控制混杂,那么我们的标准选择的协变量集也将足够。我们发现,其他常用的,标准的混杂控制没有这个属性。我们使用正式的理论因果图证明我们的结果,但结果的应用并不依赖于熟悉因果图。研究者只需要问:“协变量是治疗的原因吗?”以及“协变量是结果的原因吗”如果任一问题的答案为“是”,则纳入协变量进行混杂控制。我们讨论了一些额外的协变量选择结果,保持unconfoundedness,并可能与我们的标准时,使用的兴趣。
We propose a new criterion for confounder selection when the underlying causal structure is unknown and only limited knowledge is available. We assume all covariates being considered are pretreatment variables and that for each covariate it is known (i) whether the covariate is a cause of treatment, and (ii) whether the covariate is a cause of the outcome. The causal relationships the covariates have with one another is assumed unknown. We propose that control be made for any covariate that is either a cause of treatment or of the outcome or both. We show that irrespective of the actual underlying causal structure, if any subset of the observed covariates suffices to control for confounding then the set of covariates chosen by our criterion will also suffice. We show that other, commonly used, criteria for confounding control do not have this property. We use formal theory concerning causal diagrams to prove our result but the application of the result does not rely on familiarity with causal diagrams. An investigator simply need ask, “Is the covariate a cause of the treatment?” and “Is the covariate a cause of the outcome?” If the answer to either question is “yes” then the covariate is included for confounder control. We discuss some additional covariate selection results that preserve unconfoundedness and that may be of interest when used with our criterion.
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