Should we drop covariate cells with attrition problems?
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Cited by:
- Ferman, Bruno, 2021.
"Matching estimators with few treated and many control observations,"
Journal of Econometrics, Elsevier, vol. 225(2), pages 295-307.
- Ferman, Bruno, 2017. "Matching Estimators with Few Treated and Many Control Observations," MPRA Paper 78940, University Library of Munich, Germany.
- Bruno Ferman, 2019. "Matching Estimators with Few Treated and Many Control Observations," Papers 1909.05093, arXiv.org, revised Mar 2021.
- Ferman, Bruno & Lima, Lycia & Riva, Flávio, 2021. "Artificial Intelligence, Teacher Tasks and Individualized Pedagogy," SocArXiv qw249, Center for Open Science.
- 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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More about this item
Keywords
impact evaluation; attrition; partial identification;All these keywords.
JEL classification:
- C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
- C93 - Mathematical and Quantitative Methods - - Design of Experiments - - - Field Experiments
NEP fields
This paper has been announced in the following NEP Reports:- NEP-ECM-2017-08-13 (Econometrics)
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