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

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 estimators via extensive simulation studies and real data analyses. We found that causal effect estimates are less biased when the propensity score model includes mismeasured covariates whose true underlying values are strongly correlated with each other. However, when the measurement errors are correlated with each other, additional bias is introduced. In addition, it is beneficial to include correctly measured auxiliary variables that are correlated with confounders whose true underlying values are mismeasured in the propensity score model.