Proposes a doubly robust estimator for the average treatment effect on the treated in the eligible population when eligibility covariates are missing at random, supporting machine learning for nuisance functions with valid inference rates, demonstrated on Kaiser Permanente EHR data for bariatric and
Levis, Edward H
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Robust Causal Inference for EHR-based Studies of Point Exposures with Missingness in Eligibility Criteria
Proposes a doubly robust estimator for the average treatment effect on the treated in the eligible population when eligibility covariates are missing at random, supporting machine learning for nuisance functions with valid inference rates, demonstrated on Kaiser Permanente EHR data for bariatric and