Causal inference in observational studies can be challenging when confounders are subject to missingness. Generally, the identification of causal effects is not guaranteed even under restrictive parametric model assumptions when confounders are missing not at random. To address this, We propose a general framework to establish the identification of causal effects when confounders are subject to treatment-independent missingness, which means that the missing data mechanism is independent of the treatment, given the outcome and possibly missing confounders. We give special consideration to commonly-used models for continuous and binary outcomes and provide counterexamples when identification fails. For estimation, we provide a weighted estimation equation estimating method for model parameters and purpose three estimators for the average causal effect based on the estimated models. We evaluate the finite-sample performance of the estimators via simulations. We further illustrate the proposed method with real data sets from the National Health and Nutrition Examination Survey.

翻译：一般来说,即使在限制性的参数模型假设下,当混淆者并非随机失踪时,也不能保证确定因果关系。为了解决这个问题,我们提议了一个总框架,以确定在混杂者遭受治疗独立的缺失时,确定因果关系,这意味着缺失的数据机制与治疗无关,考虑到结果和可能缺失的混杂者。我们特别考虑通用的连续和二进制结果模型,并在识别失败时提供反样。关于估计,我们为模型参数提供加权估计方程估计方法,并基于估计模型,为平均因果关系提供三个估计值。我们通过模拟评估估计值的有限抽样性能。我们用国家健康和营养调查中的真实数据集进一步说明拟议方法。