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

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  • Kyungchul Song

    (Department of Economics, University of Pennsylvania)

Abstract

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.

Suggested Citation

  • Kyungchul Song, 2009. "Efficient Estimation of Average Treatment Effects under Treatment-Based Sampling, Second Version," PIER Working Paper Archive 10-018, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 24 May 2010.
    • Handle: RePEc:pen:papers:10-018
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    More about this item

    Keywords

    treatment-based sampling; standard stratified sampling; semi-parametric efficiency; treatment effects; optimal sampling designs;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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