IDEAS home Printed from https://ideas.repec.org/a/bpj/causin/v12y2024i1p30n1001.html
   My bibliography  Save this article

Some theoretical foundations for the design and analysis of randomized experiments

Author

Listed:
  • Shi Lei

    (Division of Biostatistics, University of California, Berkeley, CA, USA)

  • Li Xinran

    (Department of Statistics, The University of Chicago, Chicago, IL, USA)

Abstract

Neyman’s seminal work in 1923 has been a milestone in statistics over the century, which has motivated many fundamental statistical concepts and methodology. In this review, we delve into Neyman’s groundbreaking contribution and offer technical insights into the design and analysis of randomized experiments. We shall review the basic setup of completely randomized experiments and the classical approaches for inferring the average treatment effects. We shall, in particular, review more efficient design and analysis of randomized experiments by utilizing pretreatment covariates, which move beyond Neyman’s original work without involving any covariate. We then summarize several technical ingredients regarding randomizations and permutations that have been developed over the century, such as permutational central limit theorems and Berry–Esseen bounds, and we elaborate on how these technical results facilitate the understanding of randomized experiments. The discussion is also extended to other randomized experiments including rerandomization, stratified randomized experiments, matched pair experiments, and cluster randomized experiments.

Suggested Citation

  • Shi Lei & Li Xinran, 2024. "Some theoretical foundations for the design and analysis of randomized experiments," Journal of Causal Inference, De Gruyter, vol. 12(1), pages 1-30.
    • Handle: RePEc:bpj:causin:v:12:y:2024:i:1:p:30:n:1001
    DOI: 10.1515/jci-2023-0067
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/jci-2023-0067
    Download Restriction: no
    File URL: https://libkey.io/10.1515/jci-2023-0067?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. Hansen, Bruce E., 2008. "Uniform Convergence Rates For Kernel Estimation With Dependent Data," Econometric Theory, Cambridge University Press, vol. 24(3), pages 726-748, June.
    2. Judea Pearl, 2003. "Statistics and causal inference: A review," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 12(2), pages 281-345, December.
    3. Glynn, Adam N. & Quinn, Kevin M., 2010. "An Introduction to the Augmented Inverse Propensity Weighted Estimator," Political Analysis, Cambridge University Press, vol. 18(1), pages 36-56, January.
    4. Guido W. Imbens, 2004. "Nonparametric Estimation of Average Treatment Effects Under Exogeneity: A Review," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 4-29, February.
    5. Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881, November.
    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. Heigle, Julia & Pfeiffer, Friedhelm, 2019. "An analysis of selected labor market outcomes of college dropouts in Germany: A machine learning estimation approach. Research report," ZEW Expertises, ZEW - Leibniz Centre for European Economic Research, number 222378, March.
    2. Davide Viviano & Jelena Bradic, 2020. "Fair Policy Targeting," Papers 2005.12395, arXiv.org, revised Jun 2022.
    3. Christoph F. Kurz, 2022. "Augmented Inverse Probability Weighting and the Double Robustness Property," Medical Decision Making, , vol. 42(2), pages 156-167, February.
    4. 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.
    5. Caloffi, Annalisa & Freo, Marzia & Ghinoi, Stefano & Mariani, Marco & Rossi, Federica, 2022. "Assessing the effects of a deliberate policy mix: The case of technology and innovation advisory services and innovation vouchers," Research Policy, Elsevier, vol. 51(6).
    6. Shanike J. Smart & Solomon W. Polachek, 2024. "COVID-19 vaccine and risk-taking," Journal of Risk and Uncertainty, Springer, vol. 68(1), pages 25-49, February.
    7. Koch, Bernard & Sainburg, Tim & Geraldo, Pablo & JIANG, SONG & Sun, Yizhou & Foster, Jacob G., 2021. "Deep Learning of Potential Outcomes," SocArXiv aeszf, Center for Open Science.
    8. Florian Gunsilius & Yuliang Xu, 2021. "Matching for causal effects via multimarginal unbalanced optimal transport," Papers 2112.04398, arXiv.org, revised Jul 2022.
    9. Zhengyuan Zhou & Susan Athey & Stefan Wager, 2023. "Offline Multi-Action Policy Learning: Generalization and Optimization," Operations Research, INFORMS, vol. 71(1), pages 148-183, January.
    10. Plamen Nikolov & Hongjian Wang & Kevin Acker, 2020. "Wage premium of Communist Party membership: Evidence from China," Pacific Economic Review, Wiley Blackwell, vol. 25(3), pages 309-338, August.
    11. Daniel Burkhard & Christian P. R. Schmid & Kaspar Wüthrich, 2019. "Financial incentives and physician prescription behavior: Evidence from dispensing regulations," Health Economics, John Wiley & Sons, Ltd., vol. 28(9), pages 1114-1129, September.
    12. Michael Lechner, 2023. "Causal Machine Learning and its use for public policy," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 159(1), pages 1-15, December.
    13. Özler, Berk & Çelik, Çiğdem & Cunningham, Scott & Cuevas, P. Facundo & Parisotto, Luca, 2021. "Children on the move: Progressive redistribution of humanitarian cash transfers among refugees," Journal of Development Economics, Elsevier, vol. 153(C).
    14. Konan Alain N'Ghauran & Corinne Autant-Bernard, 2020. "Assessing the collaboration and network additionality of innovation policies: a counterfactual approach to the French cluster policy," Post-Print halshs-03128972, HAL.
    15. Guido W. Imbens, 2022. "Causality in Econometrics: Choice vs Chance," Econometrica, Econometric Society, vol. 90(6), pages 2541-2566, November.
    16. Andrea Mercatanti & Fan Li, 2017. "Do debit cards decrease cash demand?: causal inference and sensitivity analysis using principal stratification," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 66(4), pages 759-776, August.
    17. Dimitris Bertsimas & Nathan Kallus, 2020. "From Predictive to Prescriptive Analytics," Management Science, INFORMS, vol. 66(3), pages 1025-1044, March.
    18. Gelter, Martin & Siems, Mathias, 2024. "Elective corporate governance: Does board choice matter?," International Review of Law and Economics, Elsevier, vol. 78(C).
    19. Sun, Shanxia & Delgado, Michael & Khanna, Neha, 2017. "Hybrid Vehicles and Household Driving Behavior: Implications for Miles Traveled and Gasoline Consumption," 2017 Annual Meeting, July 30-August 1, Chicago, Illinois 258502, Agricultural and Applied Economics Association.
    20. Siying Guo & Jianxuan Liu & Qiu Wang, 2022. "Effective Learning During COVID-19: Multilevel Covariates Matching and Propensity Score Matching," Annals of Data Science, Springer, vol. 9(5), pages 967-982, October.

    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:bpj:causin:v:12:y:2024:i:1:p:30:n:1001. 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: Peter Golla (email available below). General contact details of provider: https://www.degruyter.com .

    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.