Inverse probability weighting with error-prone covariates

• 成果类型: Article
• 署名作者: McCaffrey, Daniel F.; Lockwood, J. R.; Setodji, Claude M.
• 署名单位: RAND Corporation
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/ast022
• 发表日期: 2013
• 页码: 671680
• 关键词: demystifying double robustness; propensity score; alternative strategies; regression-models; Causal Inference

摘要

Inverse probability-weighted estimators are widely used in applications where data are missing due to nonresponse or censoring and in the estimation of causal effects from observational studies. Current estimators rely on ignorability assumptions for response indicators or treatment assignment and outcomes being conditional on observed covariates which are assumed to be measured without error. However, measurement error is common for the variables collected in many applications. For example, in studies of educational interventions, student achievement as measured by standardized tests is almost always used as the key covariate for removing hidden biases, but standardized test scores may have substantial measurement errors. We provide several expressions for a weighting function that can yield a consistent estimator for population means using incomplete data and covariates measured with error. We propose a method to estimate the weighting function from data. The results of a simulation study show that the estimator is consistent and has no bias and small variance.