IDEAS home Printed from https://ideas.repec.org/a/bla/biomet/v79y2023i1p216-229.html
   My bibliography  Save this article

A matching procedure for sequential experiments that iteratively learns which covariates improve power

Author

Listed:
  • Adam Kapelner
  • Abba Krieger

Abstract

We propose a dynamic allocation procedure that increases power and efficiency when measuring an average treatment effect in sequential randomized trials exploiting some subjects' previous assessed responses. Subjects arrive sequentially and are either randomized or paired to a previously randomized subject and administered the alternate treatment. The pairing is made via a dynamic matching criterion that iteratively learns which specific covariates are important to the response. We develop estimators for the average treatment effect as well as an exact test. We illustrate our method's increase in efficiency and power over other allocation procedures in both simulated scenarios and a clinical trial dataset. An R package “SeqExpMatch” for use by practitioners is available on CRAN.

Suggested Citation

  • Adam Kapelner & Abba Krieger, 2023. "A matching procedure for sequential experiments that iteratively learns which covariates improve power," Biometrics, The International Biometric Society, vol. 79(1), pages 216-229, March.
    • Handle: RePEc:bla:biomet:v:79:y:2023:i:1:p:216-229
    DOI: 10.1111/biom.13561
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/biom.13561
    Download Restriction: no
    File URL: https://libkey.io/10.1111/biom.13561?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Kari Lock Morgan & Donald B. Rubin, 2015. "Rerandomization to Balance Tiers of Covariates," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(512), pages 1412-1421, December.
    2. Chandler, Dana & Kapelner, Adam, 2013. "Breaking monotony with meaning: Motivation in crowdsourcing markets," Journal of Economic Behavior & Organization, Elsevier, vol. 90(C), pages 123-133.
    3. Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881, October.
    4. Quan Zhou & Philip A Ernst & Kari Lock Morgan & Donald B Rubin & Anru Zhang, 2018. "Sequential rerandomization," Biometrika, Biometrika Trust, vol. 105(3), pages 745-752.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ke Zhu & Hanzhong Liu, 2023. "Pair‐switching rerandomization," Biometrics, The International Biometric Society, vol. 79(3), pages 2127-2142, September.
    2. Yang, Haoyu & Qin, Yichen & Wang, Fan & Li, Yang & Hu, Feifang, 2023. "Balancing covariates in multi-arm trials via adaptive randomization," Computational Statistics & Data Analysis, Elsevier, vol. 179(C).
    3. Hengtao Zhang & Guosheng Yin, 2021. "Response‐adaptive rerandomization," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(5), pages 1281-1298, November.
    4. Pedro Carneiro & Sokbae Lee & Daniel Wilhelm, 2020. "Optimal data collection for randomized control trials," The Econometrics Journal, Royal Economic Society, vol. 23(1), pages 1-31.
    5. Zhao, Anqi & Ding, Peng, 2024. "No star is good news: A unified look at rerandomization based on p-values from covariate balance tests," Journal of Econometrics, Elsevier, vol. 241(1).
    6. Yuehao Bai, 2022. "Optimality of Matched-Pair Designs in Randomized Controlled Trials," Papers 2206.07845, arXiv.org.
    7. Yves Tillé, 2022. "Some Solutions Inspired by Survey Sampling Theory to Build Effective Clinical Trials," International Statistical Review, International Statistical Institute, vol. 90(3), pages 481-498, December.
    8. Nicole E. Pashley & Luke W. Miratrix, 2022. "Block What You Can, Except When You Shouldn’t," Journal of Educational and Behavioral Statistics, , vol. 47(1), pages 69-100, February.
    9. Sven Resnjanskij & Jens Ruhose & Simon Wiederhold & Ludger Wößmann, 2021. "Mentoring verbessert die Arbeitsmarktchancen von stark benachteiligten Jugendlichen," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 74(02), pages 31-38, February.
    10. Alexandre Belloni & Victor Chernozhukov & Denis Chetverikov & Christian Hansen & Kengo Kato, 2018. "High-dimensional econometrics and regularized GMM," CeMMAP working papers CWP35/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    11. Dimitris Bertsimas & Agni Orfanoudaki & Rory B. Weiner, 2020. "Personalized treatment for coronary artery disease patients: a machine learning approach," Health Care Management Science, Springer, vol. 23(4), pages 482-506, December.
    12. Clément de Chaisemartin & Jaime Ramirez-Cuellar, 2024. "At What Level Should One Cluster Standard Errors in Paired and Small-Strata Experiments?," American Economic Journal: Applied Economics, American Economic Association, vol. 16(1), pages 193-212, January.
    13. Clément de Chaisemartin & Luc Behaghel, 2020. "Estimating the Effect of Treatments Allocated by Randomized Waiting Lists," Econometrica, Econometric Society, vol. 88(4), pages 1453-1477, July.
    14. Bruno Ferman & Cristine Pinto & Vitor Possebom, 2020. "Cherry Picking with Synthetic Controls," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 39(2), pages 510-532, March.
    15. Bonesrønning, Hans & Finseraas, Henning & Hardoy, Ines & Iversen, Jon Marius Vaag & Nyhus, Ole Henning & Opheim, Vibeke & Salvanes, Kari Vea & Sandsør, Astrid Marie Jorde & Schøne, Pål, 2022. "Small-group instruction to improve student performance in mathematics in early grades: Results from a randomized field experiment," Journal of Public Economics, Elsevier, vol. 216(C).
    16. Peydró, José-Luis & Jiménez, Gabriel & Kenan, Huremovic & Moral-Benito, Enrique & Vega-Redondo, Fernando, 2020. "Production and financial networks in interplay: Crisis evidence from supplier-customer and credit registers," CEPR Discussion Papers 15277, C.E.P.R. Discussion Papers.
    17. Ruoxuan Xiong & Allison Koenecke & Michael Powell & Zhu Shen & Joshua T. Vogelstein & Susan Athey, 2021. "Federated Causal Inference in Heterogeneous Observational Data," Papers 2107.11732, arXiv.org, revised Apr 2023.
    18. Marie Bjørneby & Annette Alstadsæter & Kjetil Telle, 2018. "Collusive tax evasion by employers and employees. Evidence from a randomized fi eld experiment in Norway," Discussion Papers 891, Statistics Norway, Research Department.
    19. Małgorzata W Kożusznik & José M Peiró & Aida Soriano, 2019. "Daily eudaimonic well-being as a predictor of daily performance: A dynamic lens," PLOS ONE, Public Library of Science, vol. 14(4), pages 1-24, April.
    20. Konrad Menzel, 2021. "Structural Sieves," Papers 2112.01377, arXiv.org, revised Apr 2022.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bla:biomet:v:79:y:2023:i:1:p:216-229. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=0006-341X .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.