Bias Amplification and Bias Unmasking
Bias Amplification and Bias Unmasking
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DOI：
10.1093/pan/mpw015
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发表时间：
2016-06-01
影响因子：
5.4
通讯作者：
Hill, Jennifer L.
中科院分区：
文献类型：
--
作者：
Middleton, Joel A.;Scott, Marc A.;Hill, Jennifer L.
在非实验研究的因果效应分析中，对可观察的协变量进行条件反射是试图减少未观察到的混杂偏倚的一种方法。然而，不断发展的文献表明，对某些协变量的调节可能会增加偏倚，并且这种现象背后的机制尚未得到充分探讨。我们添加到文献中的偏差增加协变量，首先引入一种方法来分解省略变量的偏差分为三个组成部分：由于未观察到的混杂因素的偏差，由于排除观察到的协变量的偏差，由于放大的偏差。这导致了两个重要的发现。虽然工具一直是偏差放大文献的主要焦点，到目前为止，我们确定的事实是，流行的方法添加组固定效应，也可以导致偏差放大。这是一个重要的发现，因为许多从业者认为固定效应是解释任何和所有组水平混杂的方便方法，并且在最坏的情况下是无害的。第二个发现引入了偏见揭露的概念，并显示了在某些情况下它如何比偏见放大更阴险。在分析介绍这些新结果后，我们使用构建的观察性安慰剂研究来说明偏差放大和真实的数据的偏差揭露。最后，我们提出了一种方法来添加偏差分解信息的图形显示的敏感性分析，以帮助从业者通过潜在的偏见放大和偏见揭露在实际应用中思考。
In the analysis of causal effects in non-experimental studies, conditioning on observable covariates is one way to try to reduce unobserved confounder bias. However, a developing literature has shown that conditioning on certain covariates may increase bias, and the mechanisms underlying this phenomenon have not been fully explored. We add to the literature on bias-increasing covariates by first introducing a way to decompose omitted variable bias into three constituent parts: bias due to an unobserved confounder, bias due to excluding observed covariates, and bias due to amplification. This leads to two important findings. Although instruments have been the primary focus of the bias amplification literature to date, we identify the fact that the popular approach of adding group fixed effects can lead to bias amplification as well. This is an important finding because many practitioners think that fixed effects are a convenient way to account for any and all group-level confounding and are at worst harmless. The second finding introduces the concept of bias unmasking and shows how it can be even more insidious than bias amplification in some cases. After introducing these new results analytically, we use constructed observational placebo studies to illustrate bias amplification and bias unmasking with real data. Finally, we propose a way to add bias decomposition information to graphical displays for sensitivity analysis to help practitioners think through the potential for bias amplification and bias unmasking in actual applications.