This paper investigates the effect that covariate measurement error has on a conventional treatment effect analysis built on an unconfoundedness restriction that embodies conditional independence restrictions in which there is conditioning on error free covariates. The approach uses small parameter asymptotic methods to obtain the approximate generic effects of measurement error. The approximations can be estimated using data on observed outcomes, the treatment indicator and error contaminated covariates providing an indication of the nature and size of measurement error effects. The approximations can be used in a sensitivity analysis to probe the potential effects of measurement error on the evaluation of treatment effects.
Centre for microdata methods and practice