Graphical Representation of Missing Data Problems
Graphical Representation of Missing Data Problems
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DOI：
10.1080/10705511.2014.937378
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发表时间：
2015-10-02
影响因子：
6
通讯作者：
Mohan, Karthika
中科院分区：
文献类型：
--
作者：
Thoemmes, Felix;Mohan, Karthika
Rubin 的经典缺失机制对于处理缺失数据和最大限度地减少因缺失而产生的偏差至关重要。然而，在缺失数据和观察到的数据之间假定某些独立性的公式表达式很难掌握。因此，应用研究人员经常依赖这些假设的非正式翻译。我们提出了缺失数据机制的图形表示，在 Mohan、Pearl 和 Tian (2013) 中得到了形式化。我们表明，图形模型提供了一种用于理解、编码和传达有关缺失过程的假设的工具。此外，我们通过几个例子证明了图论标准如何确定由于缺失数据而导致的偏差是否可能出现在某些利益估计中，以及在给定缺失过程的假设的情况下需要哪些辅助变量来控制此类偏差。
Rubin's classic missingness mechanisms are central to handling missing data and minimizing biases that can arise due to missingness. However, the formulaic expressions that posit certain independencies among missing and observed data are difficult to grasp. As a result, applied researchers often rely on informal translations of these assumptions. We present a graphical representation of missing data mechanism, formalized in Mohan, Pearl, and Tian (2013). We show that graphical models provide a tool for comprehending, encoding, and communicating assumptions about the missingness process. Furthermore, we demonstrate on several examples how graph-theoretical criteria can determine if biases due to missing data might emerge in some estimates of interests and which auxiliary variables are needed to control for such biases, given assumptions about the missingness process.