Inspection-Guided Randomization: A Flexible and Transparent Restricted Randomization Framework for Better Experimental Design.
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| Title: | Inspection-Guided Randomization: A Flexible and Transparent Restricted Randomization Framework for Better Experimental Design. |
|---|---|
| Authors: | Wang, Maggie1 (AUTHOR), Kizilcec, René F.2 (AUTHOR), Baiocchi, Michael1 (AUTHOR) |
| Source: | Journal of Educational & Behavioral Statistics. Jun2026, Vol. 51 Issue 3, p667-695. 29p. |
| Subject Terms: | *Experimental design, *Reproducible research, Randomization (Statistics), Simulation methods & models, Causal inference, Statistical sampling |
| Abstract: | Randomized experiments are considered the gold standard for estimating causal effects. However, out of the set of possible randomized assignments, some may be more likely to produce poor effect estimates and misleading conclusions. Restricted randomization is an experimental design strategy that filters out undesirable treatment assignments, but its application has primarily been limited to ensuring covariate balance in two-arm studies where the target estimand is the average treatment effect. Other experimental settings with different design desiderata and target effect estimands could also stand to benefit from a restricted randomization approach. We introduce inspection-guided randomization (IGR), a transparent and flexible framework for restricted randomization that filters out undesirable treatment assignments by inspecting assignments against analyst-specified, domain-informed design desiderata. In IGR, the acceptable treatment assignments are locked in ex ante and preregistered in the trial protocol, thus safeguarding against p-hacking and promoting reproducibility. Through illustrative simulation studies motivated by behavioral health and education interventions, we demonstrate how IGR can improve effect estimates compared to benchmark designs in experiments involving interference and group formation. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Educational & Behavioral Statistics is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Education Research Complete |
| FullText | Text: Availability: 0 |
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| Header | DbId: ehh DbLabel: Education Research Complete An: 193250188 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Inspection-Guided Randomization: A Flexible and Transparent Restricted Randomization Framework for Better Experimental Design. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Wang%2C+Maggie%22">Wang, Maggie</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kizilcec%2C+René+F%2E%22">Kizilcec, René F.</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Baiocchi%2C+Michael%22">Baiocchi, Michael</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Educational+%26+Behavioral+Statistics%22">Journal of Educational & Behavioral Statistics</searchLink>. Jun2026, Vol. 51 Issue 3, p667-695. 29p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Experimental+design%22">Experimental design</searchLink><br />*<searchLink fieldCode="DE" term="%22Reproducible+research%22">Reproducible research</searchLink><br /><searchLink fieldCode="DE" term="%22Randomization+%28Statistics%29%22">Randomization (Statistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Simulation+methods+%26+models%22">Simulation methods & models</searchLink><br /><searchLink fieldCode="DE" term="%22Causal+inference%22">Causal inference</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+sampling%22">Statistical sampling</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Randomized experiments are considered the gold standard for estimating causal effects. However, out of the set of possible randomized assignments, some may be more likely to produce poor effect estimates and misleading conclusions. Restricted randomization is an experimental design strategy that filters out undesirable treatment assignments, but its application has primarily been limited to ensuring covariate balance in two-arm studies where the target estimand is the average treatment effect. Other experimental settings with different design desiderata and target effect estimands could also stand to benefit from a restricted randomization approach. We introduce inspection-guided randomization (IGR), a transparent and flexible framework for restricted randomization that filters out undesirable treatment assignments by inspecting assignments against analyst-specified, domain-informed design desiderata. In IGR, the acceptable treatment assignments are locked in ex ante and preregistered in the trial protocol, thus safeguarding against p-hacking and promoting reproducibility. Through illustrative simulation studies motivated by behavioral health and education interventions, we demonstrate how IGR can improve effect estimates compared to benchmark designs in experiments involving interference and group formation. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Educational & Behavioral Statistics is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=ehh&AN=193250188 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3102/10769986251342292 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 29 StartPage: 667 Subjects: – SubjectFull: Experimental design Type: general – SubjectFull: Reproducible research Type: general – SubjectFull: Randomization (Statistics) Type: general – SubjectFull: Simulation methods & models Type: general – SubjectFull: Causal inference Type: general – SubjectFull: Statistical sampling Type: general Titles: – TitleFull: Inspection-Guided Randomization: A Flexible and Transparent Restricted Randomization Framework for Better Experimental Design. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Wang, Maggie – PersonEntity: Name: NameFull: Kizilcec, René F. – PersonEntity: Name: NameFull: Baiocchi, Michael IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 10769986 Numbering: – Type: volume Value: 51 – Type: issue Value: 3 Titles: – TitleFull: Journal of Educational & Behavioral Statistics Type: main |
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