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 estimation. We evaluate the performance of these estimators through simulations, and provide a real data analysis to illustrate our proposed method.

翻译：从观察性研究中进行因果推断时，若混淆变量非随机缺失，则面临挑战。在此类情形下，通常无法保证因果效应的可识别性。受实际案例启发，我们提出一种处理独立缺失假设，在该假设下建立了混淆变量非随机缺失时因果效应的识别方法。我们提出加权估计方程（WEE）方法来估计模型参数，并基于回归、倾向得分加权和双稳健估计，引入了三种平均因果效应的估计量。通过模拟实验评估了这些估计量的性能，并进行了实际数据分析以验证所提方法。