Correlation and Efficiency of Propensity Score-based Estimators for Average Causal Effects
2016 (English)In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141Article in journal (Refereed) Accepted
Propensity score based-estimators are commonly used to estimate causal effects in evaluationresearch. To reduce bias in observational studies researchers might be tempted to include many, perhaps correlated, covariates when estimating the propensity score model. Taking into account that the propensity score is estimated, this study investigates how the efficiency of matching, inverse probability weighting and doubly robust estimators change under the case of correlated covariates. Propositions regarding the large sample variances under certain assumptions on the data generating process are given. The propositions are supplemented by several numerical large sample and finite sample results from a wide range of models. The results show that the covariate correlations may increase or decrease the variances of the estimators. There are several factors that influence how correlation affects the variance of the estimators, including the choice of estimator, the strength of the confounding towards outcome and treatment, and whether a constant or non-constant causal effect is present.
Place, publisher, year, edition, pages
Taylor & Francis, 2016.
Doubly robust, Inverse probability, central nervous system, Matching, Observational study
Probability Theory and Statistics
Research subject Statistics
IdentifiersURN: urn:nbn:se:umu:diva-113905DOI: 10.1080/03610918.2015.1094091OAI: oai:DiVA.org:umu-113905DiVA: diva2:891085
FunderRiksbankens Jubileumsfond, P11-0814:1