在社会学与生物学等领域的的研究中，人们经常需要处理缺失数据或因果分析问题，我们最常用的方法是建立观测到的协变量与因变量之间的回归模型（OR模型）和倾向评分模型（PS模型）来对目标参数进行估计。双重稳健估计估计量（DR估计量）可以在PS模型和OR模型至少有一个被正确设定的情况下保持一致，这样就给人们两次得到有效估计的机会。 本文默认文中所有的缺失模式都为随机缺失。我们以对整体均值的估计为例，首先讨论了在缺失数据模型中构造DR估计量的方法。常见的数据分为非纵向数据和纵向数据两类，我们先讨论了DR估计量在非纵向数据中如何被构建的，再通过递归将其推广到纵向数据中。 而因果分析模型，我们假设模型中无混淆变量，同时所有的与处理的分配与潜在的结果相关的协变量都被检测到。我们将因果效应定义为在不同处理下观察到的潜在结果的差值，然后将分配机制量化，就可以建立与缺失数据模型的PS模型与OR模型类似的分配机制模型与反事实结果模型，进而构建类似的DR估计量。 同时，在构建DR估计量的过程中，我们发现干扰参数估计量的选择可能会对估计结果造成影响。而选择合适的干扰参数估计量能够有效地减小DR估计量的方差与偏差，提高估计量的效率。 关键字：双重稳健性;缺失数据;因果分析;倾向评分;逆概率加权

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