This paper investigates the effect that covariate measurement error has on a treatment effect analysis built on an unconfoundedness restriction in which there is conditioning on error free covariates. The approach uses small parameter asymptotic methods to obtain the approximate effects of measurement error for estimators of average treatment effects. The approximations can be estimated using data on observed outcomes, the treatment indicator and error contaminated covariates without employing additional information from validation data or instrumental variables. The results can be used in a sensitivity analysis to probe the potential effects of measurement error on the evaluation of treatment effects.
Authors
![Person graphic](/sites/default/files/styles/square_desktop/public/2022-06/IFS-person-graphic.png?itok=hWCtTSrz)
Erich Battistin
![Andrew Chesher](/sites/default/files/styles/square_desktop/public/2022-07/Andrew_Chesher.jpg?itok=_w2THcSE)
Research Fellow University College London
Andrew is the Director of the ESRC Centre for Microdata Methods and Practice (cemmap) and Professor of Economics and Economic Measurement at UCL.
Journal article details
- DOI
- 10.1016/j.jeconom.2013.10.010
- Publisher
- Elsevier
- Issue
- Volume 178, Issue 2, February 2014
Suggested citation
Battistin, E and Chesher, A. (2014). 'Treatment effect estimation with covariate measurement error' 178(2/2014)
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