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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

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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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