IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2407.03279.html
   My bibliography  Save this paper

Finely Stratified Rerandomization Designs

Author

Listed:
  • Max Cytrynbaum

Abstract

We study estimation and inference on causal parameters under finely stratified rerandomization designs, which use baseline covariates to match units into groups (e.g. matched pairs), then rerandomize within-group treatment assignments until a balance criterion is satisfied. We show that finely stratified rerandomization does partially linear regression adjustment "by design," providing nonparametric control over the stratified covariates and linear control over the rerandomized covariates. We introduce several new rerandomization schemes, allowing for imbalance metrics based on nonlinear estimators. We also propose a novel minimax scheme that uses pilot data or prior information to minimize the computational cost of rerandomization, subject to a strict bound on statistical efficiency. While the asymptotic distribution of generalized method of moments (GMM) estimators under stratified rerandomization is generically non-normal, we show how to restore asymptotic normality using ex-post linear adjustment tailored to the stratification. This enables simple asymptotically exact inference on superpopulation parameters, as well as efficient conservative inference on finite population parameters.

Suggested Citation

  • Max Cytrynbaum, 2024. "Finely Stratified Rerandomization Designs," Papers 2407.03279, arXiv.org, revised Jul 2024.
    • Handle: RePEc:arx:papers:2407.03279
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2407.03279
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Federico A. Bugni & Ivan A. Canay & Azeem M. Shaikh, 2018. "Inference Under Covariate-Adaptive Randomization," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(524), pages 1784-1796, October.
    2. repec:adr:anecst:y:2008:i:91-92:p:09 is not listed on IDEAS
    3. Robinson, Peter M, 1988. "Root- N-Consistent Semiparametric Regression," Econometrica, Econometric Society, vol. 56(4), pages 931-954, July.
    4. Yuehao Bai, 2022. "Optimality of Matched-Pair Designs in Randomized Controlled Trials," Papers 2206.07845, arXiv.org.
    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 & Jizhou Liu & Azeem M. Shaikh & Max Tabord-Meehan, 2023. "On the Efficiency of Finely Stratified Experiments," Papers 2307.15181, arXiv.org, revised Aug 2024.
    7. Alberto Abadie & Guido W. Imbens, 2008. "Estimation of the Conditional Variance in Paired Experiments," Annals of Economics and Statistics, GENES, issue 91-92, pages 175-187.
    8. Max Cytrynbaum, 2023. "Covariate Adjustment in Stratified Experiments," Papers 2302.03687, arXiv.org, revised Jul 2024.
    9. Yuehao Bai, 2022. "Optimality of Matched-Pair Designs in Randomized Controlled Trials," American Economic Review, American Economic Association, vol. 112(12), pages 3911-3940, December.
    10. Wang Q. & Linton O. & Hardle W., 2004. "Semiparametric Regression Analysis With Missing Response at Random," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 334-345, January.
    11. Yiou Li & Lulu Kang & Xiao Huang, 2021. "Covariate balancing based on kernel density estimates for controlled experiments," Statistical Theory and Related Fields, Taylor & Francis Journals, vol. 5(2), pages 102-113, April.
    12. Imbens, Guido W & Angrist, Joshua D, 1994. "Identification and Estimation of Local Average Treatment Effects," Econometrica, Econometric Society, vol. 62(2), pages 467-475, March.
    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. Yuehao Bai & Azeem M. Shaikh & Max Tabord-Meehan, 2024. "A Primer on the Analysis of Randomized Experiments and a Survey of some Recent Advances," Papers 2405.03910, arXiv.org.
    2. Bugni, Federico A. & Gao, Mengsi, 2023. "Inference under covariate-adaptive randomization with imperfect compliance," Journal of Econometrics, Elsevier, vol. 237(1).
    3. Jiang, Liang & Phillips, Peter C.B. & Tao, Yubo & Zhang, Yichong, 2023. "Regression-adjusted estimation of quantile treatment effects under covariate-adaptive randomizations," Journal of Econometrics, Elsevier, vol. 234(2), pages 758-776.
    4. Bai, Yuehao & Jiang, Liang & Romano, Joseph P. & Shaikh, Azeem M. & Zhang, Yichong, 2024. "Covariate adjustment in experiments with matched pairs," Journal of Econometrics, Elsevier, vol. 241(1).
    5. Yuehao Bai & Jizhou Liu & Max Tabord-Meehan, 2022. "Inference for Matched Tuples and Fully Blocked Factorial Designs," Papers 2206.04157, arXiv.org, revised Nov 2023.
    6. Yuehao Bai & Jizhou Liu & Azeem M. Shaikh & Max Tabord-Meehan, 2023. "On the Efficiency of Finely Stratified Experiments," Papers 2307.15181, arXiv.org, revised Aug 2024.
    7. Yuehao Bai & Hongchang Guo & Azeem M. Shaikh & Max Tabord-Meehan, 2023. "Inference in Experiments with Matched Pairs and Imperfect Compliance," Papers 2307.13094, arXiv.org, revised Jun 2024.
    8. Yuehao Bai & Meng Hsuan Hsieh & Jizhou Liu & Max Tabord-Meehan, 2022. "Revisiting the Analysis of Matched-Pair and Stratified Experiments in the Presence of Attrition," Papers 2209.11840, arXiv.org, revised Oct 2023.
    9. Jizhou Liu, 2023. "Inference for Two-stage Experiments under Covariate-Adaptive Randomization," Papers 2301.09016, arXiv.org, revised Oct 2024.
    10. Yuehao Bai & Meng Hsuan Hsieh & Jizhou Liu & Max Tabord‐Meehan, 2024. "Revisiting the analysis of matched‐pair and stratified experiments in the presence of attrition," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(2), pages 256-268, March.
    11. Laurent Davezies & Guillaume Hollard & Pedro Vergara Merino, 2024. "Revisiting Randomization with the Cube Method," Papers 2407.13613, arXiv.org.
    12. Kirill Borusyak & Peter Hull & Xavier Jaravel, 2023. "Design-Based Identification with Formula Instruments: A Review," NBER Working Papers 31393, National Bureau of Economic Research, Inc.
    13. Shoya Ishimaru, 2024. "Empirical Decomposition of the IV-OLS Gap with Heterogeneous and Nonlinear Effects," The Review of Economics and Statistics, MIT Press, vol. 106(2), pages 505-520, March.
    14. Vishal Kamat, 2017. "Identifying the Effects of a Program Offer with an Application to Head Start," Papers 1711.02048, arXiv.org, revised Aug 2023.
    15. Richard K. Crump & V. Joseph Hotz & Guido W. Imbens & Oscar A. Mitnik, 2006. "Moving the Goalposts: Addressing Limited Overlap in the Estimation of Average Treatment Effects by Changing the Estimand," NBER Technical Working Papers 0330, National Bureau of Economic Research, Inc.
    16. Alexandre Belloni & Victor Chernozhukov & Ivan Fernandez-Val & Christian Hansen, 2013. "Program evaluation with high-dimensional data," CeMMAP working papers CWP77/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    17. Chen, Songxi, 2012. "Estimation in semiparametric models with missing data," MPRA Paper 46216, University Library of Munich, Germany.
    18. Sloczynski, Tymon, 2018. "A General Weighted Average Representation of the Ordinary and Two-Stage Least Squares Estimands," IZA Discussion Papers 11866, Institute of Labor Economics (IZA).
    19. Jinglong Zhao, 2024. "Experimental Design For Causal Inference Through An Optimization Lens," Papers 2408.09607, arXiv.org, revised Aug 2024.
    20. Guido W. Imbens & Jeffrey M. Wooldridge, 2009. "Recent Developments in the Econometrics of Program Evaluation," Journal of Economic Literature, American Economic Association, vol. 47(1), pages 5-86, March.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:arx:papers:2407.03279. 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: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    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.