Making causal inferences from observational studies can be challenging when confounders are missing not at random. In such cases, identifying causal effects is often not guaranteed. Motivated by a real example, we consider a treatment-independent missingness assumption under which we establish the identification of causal effects when confounders are missing not at random. We propose a weighted estimating equation (WEE) approach for estimating model parameters and introduce three estimators for the average causal effect, based on regression, propensity score weighting, and doubly robust methods. We evaluate the performance of these estimators through simulations, and provide a real data analysis to illustrate our proposed method.

翻译：基于观察性研究进行因果推断时，若协变量存在非随机缺失，则面临挑战。在这种情况下，因果效应的识别通常无法得到保证。受实际案例启发，我们提出一种处理独立缺失假设，并在此基础上建立了协变量非随机缺失时因果效应的识别条件。我们提出加权估计方程（WEE）方法进行模型参数估计，并引入三种基于回归、倾向性得分加权和双稳健方法的平均因果效应估计量。通过模拟研究评估这些估计量的表现，并利用真实数据分析对所提方法进行说明。