Causal Inference with Multiple Misclassified Exposures: A Control Variate-Adjusted Calibration Weighting Approach

Abstract

Exposure misclassification is a common issue prevalent in studies of respiratory infections in cystic fibrosis. Throat swabs are frequently used in place of expectorated or induced sputum cultures, although they have imperfect sensitivity and specificity to detect Pseudomonas aeruginosa and Staphylococcus aureus. We develop calibration weighting and control variate estimators for causal inference with multiple misclassified binary exposures and clustered observations. The calibration approach treats misclassification as a missing data problem, achieving consistency without modeling the misclassification mechanism. The control variate adjustment integrates information from error-prone observations to reduce variance while preserving the consistency of the gold-standard estimator. We show that the resulting estimator inherits double robustness from its component estimators. We also characterize a structural ceiling on efficiency gains in the bivariate setting, where joint correct classification of both exposures limits the variance reduction achievable relative to univariate applications. Simulation studies confirm the consistency and double robustness of the proposed estimators under model misspecification. We then apply these methods to a cohort of $651$ cystic fibrosis patients ages $6$--$21$. Swab-based estimates attenuate the effect of P. aeruginosa on percent predicted FEV$_1$ by approximately $79\%$ relative to sputum-based estimates ($-1.68$ vs. $-7.88$ percentage points; $95\%$ confidence interval for sputum: $-10.08$, $-5.67$). These findings suggest that relying on throat swabs may lead to under-treatment of P. aeruginosa infections. More broadly, the methods provide a framework for causal inference with multiple misclassified exposures.