IDEAS home Printed from https://ideas.repec.org/a/eee/stapro/v149y2019icp55-62.html

Causal inference from strip-plot designs in a potential outcomes framework

Author

Listed:
  • Alqallaf, Fatemah A.
  • Huda, S.
  • Mukerjee, Rahul

Abstract

A randomization-based theory of causal inference from strip-plot designs is developed. For any treatment contrast, we propose an unbiased estimator, work out its sampling variance, and obtain a conservative variance estimator which is shown to enjoy a minimaxity property.

Suggested Citation

  • Alqallaf, Fatemah A. & Huda, S. & Mukerjee, Rahul, 2019. "Causal inference from strip-plot designs in a potential outcomes framework," Statistics & Probability Letters, Elsevier, vol. 149(C), pages 55-62.
    • Handle: RePEc:eee:stapro:v:149:y:2019:i:c:p:55-62
    DOI: 10.1016/j.spl.2019.01.027
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167715219300331
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.spl.2019.01.027?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
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Tirthankar Dasgupta & Natesh S. Pillai & Donald B. Rubin, 2015. "Causal inference from 2-super-K factorial designs by using potential outcomes," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 77(4), pages 727-753, September.
    2. Rahul Mukerjee & Tirthankar Dasgupta & Donald B. Rubin, 2018. "Using Standard Tools From Finite Population Sampling to Improve Causal Inference for Complex Experiments," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(522), pages 868-881, April.
    3. Lu, Jiannan & Deng, Alex, 2017. "On randomization-based causal inference for matched-pair factorial designs," Statistics & Probability Letters, Elsevier, vol. 125(C), pages 99-103.
    4. Lu, Jiannan, 2016. "On randomization-based and regression-based inferences for 2K factorial designs," Statistics & Probability Letters, Elsevier, vol. 112(C), pages 72-78.
    5. Lu, Jiannan, 2016. "Covariate adjustment in randomization-based causal inference for 2K factorial designs," Statistics & Probability Letters, Elsevier, vol. 119(C), pages 11-20.
    6. Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881, Enero-Abr.
    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. Haoge Chang, 2023. "Design-based Estimation Theory for Complex Experiments," Papers 2311.06891, arXiv.org, revised May 2025.
    2. Lu, Jiannan & Deng, Alex, 2017. "On randomization-based causal inference for matched-pair factorial designs," Statistics & Probability Letters, Elsevier, vol. 125(C), pages 99-103.
    3. Fangzhou Su & Peng Ding, 2021. "Model‐assisted analyses of cluster‐randomized experiments," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(5), pages 994-1015, November.
    4. Zhao, Anqi & Ding, Peng, 2021. "Covariate-adjusted Fisher randomization tests for the average treatment effect," Journal of Econometrics, Elsevier, vol. 225(2), pages 278-294.
    5. Alberto Abadie & Susan Athey & Guido W. Imbens & Jeffrey M. Wooldridge, 2020. "Sampling‐Based versus Design‐Based Uncertainty in Regression Analysis," Econometrica, Econometric Society, vol. 88(1), pages 265-296, January.
    6. Peter Z. Schochet, 2018. "Design-Based Estimators for Average Treatment Effects for Multi-Armed RCTs," Journal of Educational and Behavioral Statistics, , vol. 43(5), pages 568-593, October.
    7. Lu, Jiannan, 2016. "On randomization-based and regression-based inferences for 2K factorial designs," Statistics & Probability Letters, Elsevier, vol. 112(C), pages 72-78.
    8. 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).
    9. Jiafeng Chen, 2021. "Nonparametric Treatment Effect Identification in School Choice," Papers 2112.03872, arXiv.org, revised Dec 2025.
    10. Nicole E. Pashley & Luke W. Miratrix, 2021. "Insights on Variance Estimation for Blocked and Matched Pairs Designs," Journal of Educational and Behavioral Statistics, , vol. 46(3), pages 271-296, June.
    11. Lu, Jiannan, 2016. "Covariate adjustment in randomization-based causal inference for 2K factorial designs," Statistics & Probability Letters, Elsevier, vol. 119(C), pages 11-20.
    12. Harold D Chiang & Yukitoshi Matsushita & Taisuke Otsu, 2023. "Regression adjustment in randomized controlled trials with many covariates," STICERD - Econometrics Paper Series 627, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    13. Zach Branson & Tirthankar Dasgupta, 2020. "Sampling‐based Randomised Designs for Causal Inference under the Potential Outcomes Framework," International Statistical Review, International Statistical Institute, vol. 88(1), pages 101-121, April.
    14. Harold D Chiang & Yukitoshi Matsushita & Taisuke Otsu, 2023. "Regression adjustment in completely randomized experiments with many covariates," Papers 2302.00469, arXiv.org, revised Nov 2025.
    15. 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.
    16. 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.
    17. 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.
    18. 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.
    19. 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).
    20. 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.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    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:eee:stapro:v:149:y:2019:i:c:p:55-62. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description .

    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.