Interval estimation for treatment effects using propensity score matching.
In causal studies without random assignment of treatment, causal effects can be estimated using matched treated and control samples, where matches are obtained using estimated propensity scores. Propensity score matching can reduce bias in treatment effect estimators in cases where the matched samples have overlapping covariate distributions. Despite its application in many applied problems, there is no universally employed approach to interval estimation when using propensity score matching. In this article, we present and evaluate approaches to interval estimation when using propensity score matching.
Hill
Jennifer
J
School of International and Public Affairs, Columbia University, 420 West 118th St., 740 IAB, New York, NY 10027, USA. jh1030@columbia.edu
Reiter
Jerome P
JP
D000293:Adolescent; D001741:Black or African American; D003071:Cognition; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D005260:Female; D006801:Humans; D007223:Infant; D007230:Infant, Low Birth Weight / Q000254:growth & development; D007231:Infant, Newborn; D007234:Infant, Premature / Q000254:growth & development; D008297:Male; D012959:Socioeconomic Factors; D016896:Treatment Outcome