Propensity Score-Based Estimators With Multiple Error-Prone Covariates.

ABSTRACT: Propensity score methods are an important tool to help reduce confounding in nonexperimental studies. Most propensity score methods assume that covariates are measured without error. However, covariates are often measured with error, which leads to biased causal effect estimates if the true underlying covariates are the actual confounders. Although some groups have investigated the impact of a single mismeasured covariate on estimating a causal effect and proposed methods for handling the measurement error, fewer have investigated the case where multiple covariates are mismeasured, and we found none that discussed correlated measurement errors. In this study, we examined the consequences of multiple error-prone covariates when estimating causal effects using propensity score-based estimato

SUBMITTER: Hong H 

PROVIDER: S-EPMC6321809 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature