View in EDS HTML Full Text PDF Full Text

Balancing Weights for Estimating Treatment Effects in Educational Studies.

Saved in:
Bibliographic Details
Title: Balancing Weights for Estimating Treatment Effects in Educational Studies.
Authors: Keele, Luke1 (AUTHOR) luke.keele@gmail.com, Ben-Michael, Eli2 (AUTHOR), Lenard, Matthew3 (AUTHOR), Page, Lindsay4 (AUTHOR)
Source: Journal of Research on Educational Effectiveness. Apr-Jun2026, Vol. 19 Issue 2, p375-402. 28p.
Subject Terms: *Education research, *Preschools, Statistical weighting, Causal inference, Treatment effectiveness, Logistic regression analysis, Simulation methods & models
Geographic Terms: Wake County (N.C.)
Abstract: Inverse probability weights often are used in education to estimate causal effects in observational studies. A common practice is to estimate the probability of treatment (e.g., by logistic regression) and use the estimated treatment probabilities to estimate the weights. However, this approach can fail to lead to weights that adequately balance covariates between the treatment and control groups. Balancing weights provide one alternative method for estimating inverse probability weights that does not suffer from this limitation. Balancing weights are based on an algorithm that directly targets reducing imbalances during the estimation process, rather than improving model fit. Here, we provide an introduction to balancing weights methods. We outline the basic mechanics for the estimation of balancing weights, including the specification of a hyperparameter. We review the role of the hyperparameter and detail methods for data driven selection. We compare and contrast the analytic workflow for balancing weights with the more standard inverse probability weighting methods. We present a comparison based on a simulation study to illustrate conditions under which balancing weights perform as well as or better than weights estimated via logistic regression. We conclude with an original empirical application where we evaluate the effectiveness of Pre-K school programs in Wake County, North Carolina. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Research on Educational Effectiveness is the property of Taylor & Francis Ltd 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
Full text is not displayed to guests.
FullText Links:
  – Type: pdflink
Text:
  Availability: 1
Header DbId: ehh
DbLabel: Education Research Complete
An: 193526214
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Balancing Weights for Estimating Treatment Effects in Educational Studies.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Keele%2C+Luke%22">Keele, Luke</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> luke.keele@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Ben-Michael%2C+Eli%22">Ben-Michael, Eli</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lenard%2C+Matthew%22">Lenard, Matthew</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Page%2C+Lindsay%22">Page, Lindsay</searchLink><relatesTo>4</relatesTo> (AUTHOR)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Journal+of+Research+on+Educational+Effectiveness%22">Journal of Research on Educational Effectiveness</searchLink>. Apr-Jun2026, Vol. 19 Issue 2, p375-402. 28p.
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: *<searchLink fieldCode="DE" term="%22Education+research%22">Education research</searchLink><br />*<searchLink fieldCode="DE" term="%22Preschools%22">Preschools</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+weighting%22">Statistical weighting</searchLink><br /><searchLink fieldCode="DE" term="%22Causal+inference%22">Causal inference</searchLink><br /><searchLink fieldCode="DE" term="%22Treatment+effectiveness%22">Treatment effectiveness</searchLink><br /><searchLink fieldCode="DE" term="%22Logistic+regression+analysis%22">Logistic regression analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Simulation+methods+%26+models%22">Simulation methods & models</searchLink>
– Name: SubjectGeographic
  Label: Geographic Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Wake+County+%28N%2EC%2E%29%22">Wake County (N.C.)</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Inverse probability weights often are used in education to estimate causal effects in observational studies. A common practice is to estimate the probability of treatment (e.g., by logistic regression) and use the estimated treatment probabilities to estimate the weights. However, this approach can fail to lead to weights that adequately balance covariates between the treatment and control groups. Balancing weights provide one alternative method for estimating inverse probability weights that does not suffer from this limitation. Balancing weights are based on an algorithm that directly targets reducing imbalances during the estimation process, rather than improving model fit. Here, we provide an introduction to balancing weights methods. We outline the basic mechanics for the estimation of balancing weights, including the specification of a hyperparameter. We review the role of the hyperparameter and detail methods for data driven selection. We compare and contrast the analytic workflow for balancing weights with the more standard inverse probability weighting methods. We present a comparison based on a simulation study to illustrate conditions under which balancing weights perform as well as or better than weights estimated via logistic regression. We conclude with an original empirical application where we evaluate the effectiveness of Pre-K school programs in Wake County, North Carolina. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of Research on Educational Effectiveness is the property of Taylor & Francis Ltd 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=193526214
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1080/19345747.2025.2483775
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 28
        StartPage: 375
    Subjects:
      – SubjectFull: Education research
        Type: general
      – SubjectFull: Preschools
        Type: general
      – SubjectFull: Statistical weighting
        Type: general
      – SubjectFull: Causal inference
        Type: general
      – SubjectFull: Treatment effectiveness
        Type: general
      – SubjectFull: Logistic regression analysis
        Type: general
      – SubjectFull: Simulation methods & models
        Type: general
      – SubjectFull: Wake County (N.C.)
        Type: general
    Titles:
      – TitleFull: Balancing Weights for Estimating Treatment Effects in Educational Studies.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Keele, Luke
      – PersonEntity:
          Name:
            NameFull: Ben-Michael, Eli
      – PersonEntity:
          Name:
            NameFull: Lenard, Matthew
      – PersonEntity:
          Name:
            NameFull: Page, Lindsay
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 04
              Text: Apr-Jun2026
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 19345747
          Numbering:
            – Type: volume
              Value: 19
            – Type: issue
              Value: 2
          Titles:
            – TitleFull: Journal of Research on Educational Effectiveness
              Type: main
ResultId 1