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Treatment effect estimation with covariate measurement error

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  • Battistin, Erich
  • Chesher, Andrew

Abstract

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.

Suggested Citation

  • Battistin, Erich & Chesher, Andrew, 2014. "Treatment effect estimation with covariate measurement error," Journal of Econometrics, Elsevier, vol. 178(2), pages 707-715.
    • Handle: RePEc:eee:econom:v:178:y:2014:i:2:p:707-715
    DOI: 10.1016/j.jeconom.2013.10.010
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    Cited by:

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    2. Zhuan Pei & Jörn-Steffen Pischke & Hannes Schwandt, 2019. "Poorly Measured Confounders are More Useful on the Left than on the Right," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 37(2), pages 205-216, April.
    3. Chiara Criscuolo & Ralf Martin & Henry G. Overman & John Van Reenen, 2019. "Some Causal Effects of an Industrial Policy," American Economic Review, American Economic Association, vol. 109(1), pages 48-85, January.
    4. Chesher, Andrew, 2017. "Understanding the effect of measurement error on quantile regressions," Journal of Econometrics, Elsevier, vol. 200(2), pages 223-237.
    5. Battistin, Erich & Chesher, Andrew, 2014. "Treatment effect estimation with covariate measurement error," Journal of Econometrics, Elsevier, vol. 178(2), pages 707-715.
    6. Caliendo, Marco & Künn, Steffen & Weißenberger, Martin, 2016. "Personality traits and the evaluation of start-up subsidies," European Economic Review, Elsevier, vol. 86(C), pages 87-108.
    7. Maciej Jakubowski, 2015. "Latent variables and propensity score matching: a simulation study with application to data from the Programme for International Student Assessment in Poland," Empirical Economics, Springer, vol. 48(3), pages 1287-1325, May.
    8. Hao Dong & Daniel L. Millimet, 2020. "Propensity Score Weighting with Mismeasured Covariates: An Application to Two Financial Literacy Interventions," JRFM, MDPI, vol. 13(11), pages 1-24, November.
    9. Arulampalam, Wiji & Corradi, Valentina & Gutknecht, Daniel, 2017. "Modeling heaped duration data: An application to neonatal mortality," Journal of Econometrics, Elsevier, vol. 200(2), pages 363-377.
    10. Erich Battistin & Agar Brugiavini & Enrico Rettore & Guglielmo Weber, 2009. "The Retirement Consumption Puzzle: Evidence from a Regression Discontinuity Approach," American Economic Review, American Economic Association, vol. 99(5), pages 2209-2226, December.
    11. Andrei Zeleneev & Kirill Evdokimov, 2023. "Simple estimation of semiparametric models with measurement errors," CeMMAP working papers 10/23, Institute for Fiscal Studies.
    12. Kirill S. Evdokimov & Andrei Zeleneev, 2023. "Simple Estimation of Semiparametric Models with Measurement Errors," Papers 2306.14311, arXiv.org, revised Mar 2024.
    13. Yingying Dong, 2012. "Regression Discontinuity Applications with Rounding Errors in the Running Variable," Working Papers 111206, University of California-Irvine, Department of Economics.
    14. Toru Kitagawa & Martin Nybom & Jan Stuhler, 2018. "Measurement error and rank correlations," CeMMAP working papers CWP28/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

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    More about this item

    Keywords

    Measurement error; Potential outcomes; Parameter asymptotics; Treatment effects;
    All these keywords.

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General

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