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Efficient Estimation of Average Treatment Effects under Treatment-Based Sampling, Second Version

  • Kyungchul Song

    ()

    (Department of Economics, University of Pennsylvania)

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    Nonrandom sampling schemes are often used in program evaluation settings to improve the quality of inference. This paper considers what we call treatment-based sampling, a type of standard stratified sampling where part of the strata are based on treatment status. This paper establishes semiparametric efficiency bounds for estimators of weighted average treatment effects and average treatment effects on the treated. This paper finds that adapting the efficient estimators of Hirano, Imbens, and Ridder (2003) to treatment-based sampling does not always lead to an efficient estimator. This paper proposes efficient estimators that involve a different form of propensity score-weighting. Finally, this paper establishes an optimal design of treatment-based sampling that minimizes the semiparametric efficiency bound over the sampling designs.

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    File URL: http://economics.sas.upenn.edu/system/files/working-papers/10-018.pdf
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    Paper provided by Penn Institute for Economic Research, Department of Economics, University of Pennsylvania in its series PIER Working Paper Archive with number 10-018.

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    Length: 51 pages
    Date of creation: 06 Mar 2009
    Date of revision: 24 May 2010
    Handle: RePEc:pen:papers:10-018
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