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Nonparametric Analysis of Randomized Experiments With Missing Covariate and Outcome Data

Author

Listed:
  • Horowitz, Joel

    (University of Iowa)

  • Manski, Charles

    (Northwestern University)

Abstract

Analysis of randomized experiments with missing covariate and outcome data is problematic because the population parameters of interest are not identified unless one makes untestable assumptions about the distribution of the missing data. This paper shows how population parameters can be bounded without making untestable distributional assumptions. The bounds are sharp; that is, they exhaust all of the information that is available from the data. The bounds are illustrated with an application to data obtained from a clinical trial of treatments for hypertension. The bounds are sufficiently narrow to permit substantive conclusions to be drawn about the effects of different treatments.

Suggested Citation

  • Horowitz, Joel & Manski, Charles, 1997. "Nonparametric Analysis of Randomized Experiments With Missing Covariate and Outcome Data," Working Papers 97-16, University of Iowa, Department of Economics.
    • Handle: RePEc:uia:iowaec:97-16
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    Citations

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    Cited by:

    1. Lee & Myoung-jae, 2004. "Monotonicity Conditions and Inequality Imputation for Sample Selection and Non-Response Problems," Econometric Society 2004 Australasian Meetings 93, Econometric Society.
    2. Arntz, Melanie & Lo, Simon M. S. & Wilke, Ralf A., 2007. "Bounds analysis of competing risks : a nonparametric evaluation of the effect of unemployment benefits on migration in Germany," FDZ-Methodenreport 200704 (en), Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    3. Arthur Lewbel, 2007. "Estimation of Average Treatment Effects with Misclassification," Econometrica, Econometric Society, vol. 75(2), pages 537-551, March.
    4. Erich Battistin & Barbara Sianesi, 2006. "Misreported schooling and returns to education: evidence from the UK," CeMMAP working papers CWP07/06, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    5. David S. Lee, 2002. "Trimming for Bounds on Treatment Effects with Missing Outcomes," NBER Technical Working Papers 0277, National Bureau of Economic Research, Inc.
    6. Arie Beresteanu & Francesca Molinari, 2008. "Asymptotic Properties for a Class of Partially Identified Models," Econometrica, Econometric Society, vol. 76(4), pages 763-814, July.
    7. Rosen, Adam M., 2008. "Confidence sets for partially identified parameters that satisfy a finite number of moment inequalities," Journal of Econometrics, Elsevier, vol. 146(1), pages 107-117, September.
    8. Christopher R. Bollinger & Barry T. Hirsch, 2006. "Match Bias from Earnings Imputation in the Current Population Survey: The Case of Imperfect Matching," Journal of Labor Economics, University of Chicago Press, vol. 24(3), pages 483-520, July.
    9. Guido W. Imbens & Charles F. Manski, 2004. "Confidence Intervals for Partially Identified Parameters," Econometrica, Econometric Society, vol. 72(6), pages 1845-1857, November.
    10. Bijwaard, Govert E. & Ridder, Geert, 2005. "Correcting for selective compliance in a re-employment bonus experiment," Journal of Econometrics, Elsevier, vol. 125(1-2), pages 77-111.
    11. Charles F. Manski, 2000. "Using Studies of Treatment Response to Inform Treatment Choice in Heterogeneous Populations," NBER Technical Working Papers 0263, National Bureau of Economic Research, Inc.
    12. Robert P. Sherman & Jeff Dominitz, 2006. "Identification and estimation of bounds on school performance measures: a nonparametric analysis of a mixture model with verification," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(8), pages 1295-1326.

    More about this item

    Keywords

    Identification; Attrition; Bounds;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General

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