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：从观察性研究中推断因果关系，当混杂因素非随机缺失时往往面临挑战。此类情况下，因果效应的识别通常无法得到保证。受实际案例启发，我们提出一种处理变量独立的缺失假设，在此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。我们提出加权估计方程法用于模型参数估计，并基于回归、倾向得分加权和双稳健估计引入三种平均因果效应估计量。通过模拟实验评估这些估计量的性能，并结合实际数据分析验证所提方法。