J.R. Lockwood

Weighting and matching estimators using observed covariates to adjust for differences among non-equivalent groups are commonly used to estimate causal effects with observational data. The theorems supporting these approaches require that covariates necessary to adjust for group differences be measured without error. However, covariate measurement error is common in many applications, including education research where test scores are typically important covariates but measure latent achievement constructs with error. This presentation will review recent work on approaches that can be used to correct weighting and matching estimators for covariate measurement error. It argues that a general strategy exists to correct weighting estimators, but matching estimators cannot generally be fixed.