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Summary

The current statistical literature on causal inference is primarily concerned with population means of potential outcomes, while the current statistical practice also involves other meaningful quantities such as quantiles. Motivated by the Consortium on Safe Labor (CSL), a large observational study of obstetric labor progression, we propose and compare methods for estimating marginal quantiles of potential outcomes as well as quantiles among the treated. By adapting existing methods and techniques, we derive estimators based on outcome regression (OR), inverse probability weighting, and stratification, as well as a doubly robust (DR) estimator. By incorporating stratification into the DR estimator, we further develop a hybrid estimator with enhanced numerical stability at the expense of a slight bias under misspecification of the OR model. The proposed methods are illustrated with the CSL data and evaluated in simulation experiments mimicking the CSL.

Double robustness, Inverse probability weighting, Missing data, Outcome regression, Propensity score, Stratification, Subclassification
© 2011, The International Biometric Society No Claim to original US government works
This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://dbpia.nl.go.kr/journals/pages/open_access/funder_policies/chorus/standard_publication_model)
Issue Section:
Biometric Methodology
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