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

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  • 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
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    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.
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    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.

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