Testing for heterogeneous treatment effects in experimental data: false discovery risks and correction procedures.
We review the statistical models applied to test for heterogeneous treatment effects in the recent empirical literature, with a particular focus on data from randomised field experiments. We show that testing for heterogeneous treatment effects is highly common, and likely to result in a large numbe...
| Publicado en: | Journal of Development Effectiveness Vol. 6; no. 1; pp. 44 - 58 |
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| Autores principales: | , , |
| Formato: | Artículo |
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Taylor & Francis Ltd
Mar2014
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=95048149&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 95048149 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 19439342 8VWF jtl: Journal of Development Effectiveness issn: 19439342 maglogo: N pubinfo: dt: Mar2014 vid: 6 iid: 1 pid: 377 pub: Taylor & Francis Ltd artinfo: ui: 95048149 10.1080/19439342.2013.875054 ppf: 44 ppct: 14 formats: tig: atl: Testing for heterogeneous treatment effects in experimental data: false discovery risks and correction procedures. aug: au: Fink, Günther McConnell, Margaret Vollmer, Sebastian affil: Department of Global Health and Population, Harvard School of Public Health, 665 Huntington Avenue, Boston, MA02115, USA Department of Economics, University of Göttingen, Platz der Göttinger Sieben 3, 37073Göttingen, Germany su: Analysis of variance Heterogeneous computing Statistical hypothesis testing Mathematical optimization Experimental design sug: subj: Analysis of variance Heterogeneous computing Statistical hypothesis testing Mathematical optimization Experimental design keyword: experimental design heterogeneous treatment effects multiple testing randomised field experiments experimental design heterogeneous treatment effects multiple testing randomised field experiments ab: We review the statistical models applied to test for heterogeneous treatment effects in the recent empirical literature, with a particular focus on data from randomised field experiments. We show that testing for heterogeneous treatment effects is highly common, and likely to result in a large number of false discoveries when conventional decision rules are applied. We demonstrate that applying correction procedures developed in the statistics literature can fully address this issue, and discuss the implications of multiple testing adjustments for power calculations and experimental design. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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