<p>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.</p>
Authors
Erich Battistin
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.
Working Paper details
- DOI
- 10.1920/wp.cem.2009.2509
- Publisher
- IFS
Suggested citation
Battistin, E and Chesher, A. (2009). Treatment effect estimation with covariate measurement error. London: IFS. Available at: https://ifs.org.uk/publications/treatment-effect-estimation-covariate-measurement-error (accessed: 19 May 2024).
Related documents
More from IFS
Understand this issue
Where next for the state pension?
13 December 2023
Social mobility and wealth
12 December 2023
Autumn Statement 2023: IFS analysis
23 November 2023
Policy analysis
The past and future of UK health spending
14 May 2024
Recent trends in and the outlook for health-related benefits
19 April 2024
Progression of nurses within the NHS
12 April 2024
Academic research
Keeping the peace whilst getting your way: Information, persuasion and intimate partner violence
17 May 2024
The role of hospital networks in individual mortality
13 May 2024
Forced displacement, mental health, and child development: Evidence from Rohingya refugees
10 May 2024