Double Robust Weighted Regression with Missing Confounders

TL;DR

This paper introduces a doubly robust weighted regression method for causal effect estimation in observational studies with missing confounders, enhancing robustness over existing approaches.

Contribution

It develops a novel MI-WOLS estimator that combines propensity score weighting with outcome regression, achieving consistency under weaker assumptions.

Findings

Simulation studies show negligible bias and accurate variance estimation.

The method maintains consistency when either the treatment or outcome model is correct.

Application to kidney function data demonstrates practical utility and interpretability.

Abstract

Missing confounders are common in observational studies and present fundamental challenges for causal effect estimation by weakening identification and increasing sensitivity to model misspecification. Within the missing-indicator framework, existing methods 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 weighted ordinary least squares (MI-WOLS) estimator with partially observed confounders. The MI-WOLS estimator incorporates the treatment assignment mechanism, commonly known as the propensity score model, into the weighting structure of the outcome regression. Building on the missing-indicator framework, we define propensity score based regression weights that satisfy a covariate-balancing condition in the presence of confounder missingness. Under the missingness-strongly-ignorable treatment allocation assumption and assuming either a Conditionally Independent Treatment or Conditionally Independent Outcome structure, the MI-WOLS estimator is consistent when at least the treatment or the outcome model is correctly specified. Simulation studies support the theoretical robustness of the MI-WOLS estimator, demonstrating negligible bias, accurate sandwich-based variance estimation, and near-nominal coverage probability across a wide range of data-generating scenarios. An illustrative application to kidney function outcomes further demonstrates the interpretability and practical feasibility of the method, offering a flexible, doubly robust alternative to existing singly robust estimators.