Designing Randomized Experiments to Predict Unit-Specific Treatment Effects.
When evaluating a program or policy, a randomized experiment is typically designed to test a single confirmatory hypothesis about the average treatment effect, although subgroup and moderator effects may also be explored. The resulting average treatment effect estimate is then reported in research c...
| Publicado en: | Statistics & Public Policy Vol. 12; no. 1; pp. 1 - 19 |
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| Autores principales: | , |
| Formato: | Artículo |
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Taylor & Francis Ltd
Dec2025
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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=190411197&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 190411197 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 2330443X GQIB jtl: Statistics & Public Policy issn: 2330443X maglogo: N pubinfo: dt: Dec2025 vid: 12 iid: 1 pid: 377 pub: Taylor & Francis Ltd artinfo: ui: 190411197 10.1080/2330443X.2025.2505485 ppf: 1 ppct: 18 formats: tig: atl: Designing Randomized Experiments to Predict Unit-Specific Treatment Effects. aug: au: Tipton, Elizabeth Mamakos, Michalis affil: Department of Statistics and Data Science, Northwestern University, Evanston, IL Kellogg School of Management, Northwestern University, Evanston, IL su: Clinical trials Treatment effectiveness Generalizability theory Sampling errors Regression analysis sug: subj: Clinical trials Research and Development in the Physical, Engineering, and Life Sciences (except Biotechnology) Treatment effectiveness Generalizability theory Sampling errors Regression analysis keyword: Design Experiment Power analysis Prediction Design Experiment Power analysis Prediction ab: When evaluating a program or policy, a randomized experiment is typically designed to test a single confirmatory hypothesis about the average treatment effect, although subgroup and moderator effects may also be explored. The resulting average treatment effect estimate is then reported in research clearinghouses and used to inform policy and practice decisions for units not in the study. This use suggests that the purpose of these randomized trials is not only the testing of hypotheses, but rather the prediction of treatment effects for a broad set of units in a population. In this article, we consider the optimal design of a randomized experiment focused on the prediction of unit-specific effects. We consider how different sampling processes and models affect the mean squared error of these predictions. The results indicate, for example, that problems of generalizability—differences between study samples and target populations—can greatly increase prediction error. We also identify the conditions under which the best unit-specific treatment effect is the average treatment effect estimate. Throughout, we use simple regression models to connect the predictive and hypothesis testing literatures and to provide implications for the design of randomized experiments. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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