title: Causal inference on quantiles with an obstetric application.
abstract: 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.
authors.affiliation: Division of Biostatistics, Office of Surveillance and Biometrics, Center for Devices and Radiological Health, Food and Drug Administration, Silver Spring, Maryland 20993, USA. zhiwei.zhang@fda.hhs.gov
authors.forename: Zhiwei
authors.initials: Z
authors.lastname: Zhang
authors.forename: Zhen
authors.initials: Z
authors.lastname: Chen
authors.forename: James F
authors.initials: JF
authors.lastname: Troendle
authors.forename: Jun
authors.initials: J
authors.lastname: Zhang
mesh_terms: D015360:Analgesia, Epidural; D001699:Biometry / Q000379:methods; D015984:Causality; D005260:Female; D006801:Humans; D007231:Infant, Newborn; D007748:Labor Stage, Second; D007743:Labor, Obstetric; D016014:Linear Models; D015233:Models, Statistical; D011247:Pregnancy; D011256:Pregnancy Outcome; D012044:Regression Analysis; D013997:Time Factors