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Estimation of Average Treatment Effects With Misclassification

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  • Arthur Lewbel

    () (Boston College)

Abstract

This paper considers identification and estimation of the marginal effect of a mismeasured binary regressor in a nonparametric regression, or the conditional average effect of a binary treatment or policy on some outcome where treatment may be misclassified. Misclassification probabilities and the true probability of treatment are also nonparametrically identified. Misclassification occurs when treatment is measured with error, that is, some units are reported to have received treatment when they actually have not, and vice versa. The identifying assumption is existence of a variable that affects the decision to treat (the binary regressor) and satisfies some conditional independence assumptions. This variable could be an instrument or a second mismeasure of treatment. Estimation is either ordinary GMM or a proposed local GMM, which can be used generally to nonparametrically estimate functions based on conditional moment restrictions. An empirical application estimating returns to schooling is provided.

Suggested Citation

  • Arthur Lewbel, 2003. "Estimation of Average Treatment Effects With Misclassification," Boston College Working Papers in Economics 556, Boston College Department of Economics, revised 04 Sep 2006.
  • Handle: RePEc:boc:bocoec:556
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    References listed on IDEAS

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    More about this item

    Keywords

    binary regressor; program evaluation; treatment effects; misclassification; contamination bias; measurement error; binary choice; binomial response.;

    JEL classification:

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

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