Instrumental variables as bias amplifiers with general outcome and confounding.
Instrumental variables as bias amplifiers with general outcome and confounding.
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DOI：
10.1093/biomet/asx009
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发表时间：
2017-06-01
期刊：
影响因子：
2.7
通讯作者：
Robins JM
中科院分区：
文献类型：
--
作者：
Ding P;VanderWeele TJ;Robins JM
通过观察性研究得出因果推论是许多学科的核心支柱。确定因果效应的一个充分条件是，治疗-结局关系在观察到的协变量的条件下是无混杂的。人们通常认为，我们所依赖的协变量越多，这种无混淆性假设就越合理。这种信念对实际的因果推理产生了巨大的影响，这表明我们应该对所有预处理协变量进行调整。然而，当治疗和结局之间存在不可测量的混杂时，调整某些治疗前协变量的估计值可能比不调整该协变量的估计值具有更大的偏倚。这种协变量称为偏差放大器，包括独立于混杂因素且仅通过治疗影响结果的工具变量。以前，这种现象的理论结果已经建立了线性模型。我们填补了这一空白，在文献中提供了一个一般的理论，表明这种现象发生在一个广泛的一类模型满足一定的单调性假设。
Drawing causal inference with observational studies is the central pillar of many disciplines. One sufficient condition for identifying the causal effect is that the treatment-outcome relationship is unconfounded conditional on the observed covariates. It is often believed that the more covariates we condition on, the more plausible this unconfoundedness assumption is. This belief has had a huge impact on practical causal inference, suggesting that we should adjust for all pretreatment covariates. However, when there is unmeasured confounding between the treatment and outcome, estimators adjusting for some pretreatment covariate might have greater bias than estimators that do not adjust for this covariate. This kind of covariate is called a bias amplifier, and includes instrumental variables that are independent of the confounder and affect the outcome only through the treatment. Previously, theoretical results for this phenomenon have been established only for linear models. We fill this gap in the literature by providing a general theory, showing that this phenomenon happens under a wide class of models satisfying certain monotonicity assumptions.
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