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：基于观测数据进行因果推断时，若混杂因素存在非随机缺失，则面临挑战。在此类情境下，通常无法保证因果效应的可识别性。受实际案例启发，我们提出一种处理缺失与处理变量独立的假设，在该假设下建立了混杂因素非随机缺失时因果效应的识别条件。我们采用加权估计方程方法估计模型参数，并基于回归、倾向评分加权及双重稳健估计三种策略，构建了平均因果效应的三类估计量。通过模拟实验评估了这些估计量的性能，并利用真实数据分析验证了所提方法的有效性。