New calibration weighting and control variate estimators for causal inference with multiple misclassified binary exposures achieve consistency and double robustness without modeling the misclassification process, with application showing 69% attenuation of Pseudomonas effect on FEV1 when using swabs
Combining multiple imputation with raking of weights: An efficient and robust approach in the setting of nearly true models.Statistics in Medicine, 40(30):6777–6791, 2021
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Causal Inference with Multiple Misclassified Exposures: A Control Variate-Adjusted Calibration Weighting Approach
New calibration weighting and control variate estimators for causal inference with multiple misclassified binary exposures achieve consistency and double robustness without modeling the misclassification process, with application showing 69% attenuation of Pseudomonas effect on FEV1 when using swabs