Comparison of methods for handling covariate missingness in propensity score estimation with a binary exposure

Comparison of methods for handling covariate missingness in propensity score estimation with a binary exposure

Abstract Background Causal effect estimation with observational data is subject to bias due to confounding, which is often controlled for using propensity scores. One unresolved issue in propensity score estimation is how to handle missing values in covariates. Method Several approaches have been pr...

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Bibliographic Details
Main Authors: Donna L. Coffman, Jiangxiu Zhou, Xizhen Cai
Format: Article
Language:English
Published: BMC 2020-06-01
Series:BMC Medical Research Methodology
Subjects:
Propensity scores
Missing data
Causal inference
Generalized boosted models
Online Access:http://link.springer.com/article/10.1186/s12874-020-01053-4

Internet

http://link.springer.com/article/10.1186/s12874-020-01053-4

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