Efficient Estimation of Average Treatment Effects under Treatment-Based Sampling, Second Version
AbstractNonrandom 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.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoPaper provided by Penn Institute for Economic Research, Department of Economics, University of Pennsylvania in its series PIER Working Paper Archive with number 10-018.
Length: 51 pages
Date of creation: 06 Mar 2009
Date of revision: 24 May 2010
treatment-based sampling; standard stratified sampling; semi-parametric efficiency; treatment effects; optimal sampling designs;
Find related papers by 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
You can help add them by filling out this form.
reading list or among the top items on IDEAS.Access and download statisticsgeneral information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Dolly Guarini).
If references are entirely missing, you can add them using this form.