Doubly Robust Weighted Regression for Causal Inference under Confounder Missingness
混杂变量缺失下因果推断的双重稳健加权回归
Missingness in confounders is common in observational studies and presents substantial challenges for causal effect estimation by weakening identification and increasing sensitivity to model misspecification. Within the missing-indicator framework, existing approaches typically rely on a single working model and achieve consistency only when that model is correctly specified and are therefore singly robust. In this article, we develop a doubly robust missing indicator…