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Should we drop covariate cells with attrition problems?

Author

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  • Ferman, Bruno
  • Ponczek, Vladimir

Abstract

It is well known that sample attrition can lead to inconsistent treatment effect estimators even in randomized control trials. Standard solutions to attrition problems either rely on strong assumptions on the attrition mechanisms or consider the estimation of bounds, which may be uninformative if attrition problems are severe. In this paper, we analyze strategies of focusing the analysis on subsets of the data with less observed attrition problems. We show that these strategies are asymptotically valid when the number of observations in each covariate cell goes to infinity. However, they can lead to important distortions when the number of observations per covariate cell is finite.

Suggested Citation

  • Ferman, Bruno & Ponczek, Vladimir, 2017. "Should we drop covariate cells with attrition problems?," MPRA Paper 80686, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:80686
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    References listed on IDEAS

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    Cited by:

    1. Ferman, Bruno & Lima, Lycia & Riva, Flávio, 2021. "Artificial Intelligence, Teacher Tasks and Individualized Pedagogy," SocArXiv qw249, Center for Open Science.
    2. Ferman, Bruno, 2021. "Matching estimators with few treated and many control observations," Journal of Econometrics, Elsevier, vol. 225(2), pages 295-307.
    3. Ferman, Bruno & Lima, Lycia & Riva, Flavio, 2020. "Experimental Evidence on Artificial Intelligence in the Classroom," MPRA Paper 103934, University Library of Munich, Germany.

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    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C93 - Mathematical and Quantitative Methods - - Design of Experiments - - - Field Experiments

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