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

Kyungchul Song ()
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Kyungchul Song: Department of Economics, University of Pennsylvania

PIER Working Paper Archive from Penn Institute for Economic Research, 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.

Keywords: treatment-based sampling; standard stratified sampling; semi-parametric efficiency; treatment effects; optimal sampling designs (search for similar items in EconPapers)
JEL-codes: C12 C14 C52 (search for similar items in EconPapers)
Pages: 51 pages
Date: 2009-03-06, Revised 2010-05-24
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Persistent link: https://EconPapers.repec.org/RePEc:pen:papers:10-018

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