IDEAS home Printed from https://ideas.repec.org/a/eee/stapro/v112y2016icp72-78.html
   My bibliography  Save this article

On randomization-based and regression-based inferences for 2K factorial designs

Author

Listed:
  • Lu, Jiannan

Abstract

We extend the randomization-based causal inference framework in Dasgupta et al. (2015) for general 2K factorial designs, and demonstrate the equivalence between regression-based and randomization-based inferences. Consequently, we justify the use of regression-based methods in 2K factorial designs from a finite-population perspective.

Suggested Citation

  • Lu, Jiannan, 2016. "On randomization-based and regression-based inferences for 2K factorial designs," Statistics & Probability Letters, Elsevier, vol. 112(C), pages 72-78.
    • Handle: RePEc:eee:stapro:v:112:y:2016:i:c:p:72-78
    DOI: 10.1016/j.spl.2016.01.010
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167715215304144
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.spl.2016.01.010?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 search 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. repec:mpr:mprres:6573 is not listed on IDEAS
    3. Lu, Jiannan & Ding, Peng & Dasgupta, Tirthankar, 2015. "Construction of alternative hypotheses for randomization tests with ordinal outcomes," Statistics & Probability Letters, Elsevier, vol. 107(C), pages 348-355.
    4. Samii, Cyrus & Aronow, Peter M., 2012. "On equivalencies between design-based and regression-based variance estimators for randomized experiments," Statistics & Probability Letters, Elsevier, vol. 82(2), pages 365-370.
    5. Peter Z. Schochet, "undated". "Is Regression Adjustment Supported by the Neyman Model for Causal Inference? (Presentation)," Mathematica Policy Research Reports abfc39d59c714499b2fe42f68, Mathematica Policy Research.
    6. Peter Z. Schochet, "undated". "Is Regression Adjustment Supported By the Neyman Model for Causal Inference?," Mathematica Policy Research Reports 782da2242fba458eb61752f96, Mathematica Policy Research.
    7. MacKinnon, James G. & White, Halbert, 1985. "Some heteroskedasticity-consistent covariance matrix estimators with improved finite sample properties," Journal of Econometrics, Elsevier, vol. 29(3), pages 305-325, September.
    8. Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881, October.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. Lu, Jiannan, 2016. "Covariate adjustment in randomization-based causal inference for 2K factorial designs," Statistics & Probability Letters, Elsevier, vol. 119(C), pages 11-20.
    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. 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.

    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. 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.
    2. Susan Athey & Guido Imbens, 2016. "The Econometrics of Randomized Experiments," Papers 1607.00698, arXiv.org.
    3. Peter Z. Schochet, 2020. "Analyzing Grouped Administrative Data for RCTs Using Design-Based Methods," Journal of Educational and Behavioral Statistics, , vol. 45(1), pages 32-57, February.
    4. Joel A. Middleton, 2021. "Unifying Design-based Inference: On Bounding and Estimating the Variance of any Linear Estimator in any Experimental Design," Papers 2109.09220, arXiv.org.
    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. Peter Z. Schochet, "undated". "Statistical Theory for the RCT-YES Software: Design-Based Causal Inference for RCTs," Mathematica Policy Research Reports a0c005c003c242308a92c02dc, Mathematica Policy Research.
    7. Haoge Chang & Joel Middleton & P. M. Aronow, 2021. "Exact Bias Correction for Linear Adjustment of Randomized Controlled Trials," Papers 2110.08425, arXiv.org, revised Oct 2021.
    8. Alberto Abadie & Susan Athey & Guido W. Imbens & Jeffrey M. Wooldridge, 2017. "Sampling-based vs. Design-based Uncertainty in Regression Analysis," Papers 1706.01778, arXiv.org, revised Jun 2019.
    9. Alberto Abadie & Susan Athey & Guido W. Imbens & Jeffrey M. Wooldridge, 2014. "Finite Population Causal Standard Errors," NBER Working Papers 20325, National Bureau of Economic Research, Inc.
    10. Aronow Peter M. & Middleton Joel A., 2013. "A Class of Unbiased Estimators of the Average Treatment Effect in Randomized Experiments," Journal of Causal Inference, De Gruyter, vol. 1(1), pages 135-154, June.
    11. Tim Kautz & Peter Z. Schochet & Charles Tilley, "undated". "Comparing Impact Findings from Design-Based and Model-Based Methods: An Empirical Investigation," Mathematica Policy Research Reports b7656ddce20f4007b71836e99, Mathematica Policy Research.
    12. repec:mpr:mprres:7638 is not listed on IDEAS
    13. Sviták, Jan & Tichem, Jan & Haasbeek, Stefan, 2021. "Price effects of search advertising restrictions," International Journal of Industrial Organization, Elsevier, vol. 77(C).
    14. repec:mpr:mprres:6286 is not listed on IDEAS
    15. Kirill Borusyak & Xavier Jaravel & Jann Spiess, 2021. "Revisiting Event Study Designs: Robust and Efficient Estimation," Papers 2108.12419, arXiv.org, revised Jan 2024.
    16. Marynia Kolak & Luc Anselin, 2020. "A Spatial Perspective on the Econometrics of Program Evaluation," International Regional Science Review, , vol. 43(1-2), pages 128-153, January.
    17. repec:mpr:mprres:7273 is not listed on IDEAS
    18. Kenneth Fortson & Natalya Verbitsky-Savitz & Emma Kopa & Philip Gleason, 2012. "Using an Experimental Evaluation of Charter Schools to Test Whether Nonexperimental Comparison Group Methods Can Replicate Experimental Impact Estimates," Mathematica Policy Research Reports 27f871b5b7b94f3a80278a593, Mathematica Policy Research.
    19. repec:mpr:mprres:6094 is not listed on IDEAS
    20. John Deke, 2016. "Design and Analysis Considerations for Cluster Randomized Controlled Trials That Have a Small Number of Clusters," Evaluation Review, , vol. 40(5), pages 444-486, October.
    21. Melissa A. Clark & Philip Gleason & Christina Clark Tuttle & Marsha K. Silverberg, 2011. "Do Charter Schools Improve Student Achievement? Evidence from a National Randomized Study," Mathematica Policy Research Reports af41392138504f369930e6f2b, Mathematica Policy Research.
    22. 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.
    23. MacKinnon, James G. & Nielsen, Morten Ørregaard & Webb, Matthew D., 2023. "Cluster-robust inference: A guide to empirical practice," Journal of Econometrics, Elsevier, vol. 232(2), pages 272-299.
    24. Dmitry Arkhangelsky & Susan Athey & David A. Hirshberg & Guido W. Imbens & Stefan Wager, 2021. "Synthetic Difference-in-Differences," American Economic Review, American Economic Association, vol. 111(12), pages 4088-4118, December.

    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:112:y:2016:i:c:p:72-78. 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.