非差分测量误差混杂因子导致偏倚衰减的一般条件
A general condition for bias attenuation by a nondifferentially mismeasured confounder
AI总结：
本文证明在非差分测量误差混杂因子下，调整测量误差混杂因子通常能减少因果效应估计偏倚，扩展了先前文献的适用条件。
摘要：
在现实研究中，所收集的混杂因子可能受到测量误差的影响。尽管混杂因子的测量误差通常是无意中产生的——源于人为疏忽或机器不精确等来源——但故意的测量误差也会发生，并且变得越来越普遍。例如，在2020年美国人口普查中，为了缓解隐私担忧，对测量数据添加了噪声。诸如收入或年龄等敏感变量常常被部分审查，仅知道其取值范围。在此类情况下，获得二元处理因果效应的有效估计可能是不可能的，因为混杂因子的测量误差构成了对无未测量混杂假设的违反。一个自然的问题是，常见的仅调整测量误差混杂因子的做法是否合理。在本文中，我们对此问题给出肯定回答，并证明在先前文献未涵盖的许多现实场景中，与不调整相比，调整测量误差混杂因子可减少偏倚。
英文摘要：
In real-world studies, the collected confounders may suffer from measurement error. Although mismeasurement of confounders is typically unintentional -- originating from sources such as human oversight or imprecise machinery -- deliberate mismeasurement also occurs and is becoming increasingly more common. For example, in the 2020 U.S. Census, noise was added to measurements to assuage privacy concerns. Sensitive variables such as income or age are oftentimes partially censored and are only known up to a range of values. In such settings, obtaining valid estimates of the causal effect of a binary treatment can be impossible, as mismeasurement of confounders constitutes a violation of the no unmeasured confounding assumption. A natural question is whether the common practice of simply adjusting for the mismeasured confounder is justifiable. In this article, we answer this question in the affirmative and demonstrate that in many realistic scenarios not covered by previous literature, adjusting for the mismeasured confounders reduces bias compared to not adjusting.
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