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A Class of Unbiased Estimators of the Average Treatment Effect in Randomized Experiments

Author

Listed:
  • Aronow Peter M.

    (Yale University, New Haven, CT 06511, USA)

  • Middleton Joel A.

    (New York University, 246 Greene Street, NY, NY 10003)

Abstract

We derive a class of design-based estimators for the average treatment effect that are unbiased whenever the treatment assignment process is known. We generalize these estimators to include unbiased covariate adjustment using any model for outcomes that the analyst chooses. We then provide expressions and conservative estimators for the variance of the proposed estimators.

Suggested Citation

  • 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.
    • Handle: RePEc:bpj:causin:v:1:y:2013:i:1:p:135-154:n:4
    DOI: 10.1515/jci-2012-0009
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    References listed on IDEAS

    as
    1. Joshua D. Angrist & Jörn-Steffen Pischke, 2009. "Mostly Harmless Econometrics: An Empiricist's Companion," Economics Books, Princeton University Press, edition 1, number 8769, December.
    2. Peter Z. Schochet, "undated". "Is Regression Adjustment Supported by the Neyman Model for Causal Inference? (Presentation)," Mathematica Policy Research Reports abfc39d59c714499b2fe42f68, Mathematica Policy Research.
    3. Middleton, Joel A., 2008. "Bias of the regression estimator for experiments using clustered random assignment," Statistics & Probability Letters, Elsevier, vol. 78(16), pages 2654-2659, November.
    4. Peter Z. Schochet, "undated". "Is Regression Adjustment Supported By the Neyman Model for Causal Inference?," Mathematica Policy Research Reports 782da2242fba458eb61752f96, Mathematica Policy Research.
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    6. Green, Donald P. & Vavreck, Lynn, 2008. "Analysis of Cluster-Randomized Experiments: A Comparison of Alternative Estimation Approaches," Political Analysis, Cambridge University Press, vol. 16(2), pages 138-152, April.
    7. 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.
    8. Arceneaux, Kevin & Nickerson, David W., 2009. "Modeling Certainty with Clustered Data: A Comparison of Methods," Political Analysis, Cambridge University Press, vol. 17(2), pages 177-190, April.
    9. Hansen, Ben B. & Bowers, Jake, 2009. "Attributing Effects to a Cluster-Randomized Get-Out-the-Vote Campaign," Journal of the American Statistical Association, American Statistical Association, vol. 104(487), pages 873-885.
    10. Joshua D. Angrist, 1998. "Estimating the Labor Market Impact of Voluntary Military Service Using Social Security Data on Military Applicants," Econometrica, Econometric Society, vol. 66(2), pages 249-288, March.
    11. Joshua D. Angrist & Victor Lavy, 2002. "The Effect of High School Matriculation Awards: Evidence from Randomized Trials," NBER Working Papers 9389, National Bureau of Economic Research, Inc.
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    Citations

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    Cited by:

    1. 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.
    2. 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.

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