Propensity Score-Matching Methods For Nonexperimental Causal Studies
AbstractThis paper considers causal inference and sample selection bias in nonexperimental settings in which (i) few units in the nonexperimental comparison group are comparable to the treatment units, and (ii) selecting a subset of comparison units similar to the treatment units is difficult because units must be compared across a high-dimensional set of pretreatment characteristics. We discuss the use of propensity score-matching methods, and implement them using data from the National Supported Work experiment. Following LaLonde (1986), we pair the experimental treated units with nonexperimental comparison units from the CPS and PSID, and compare the estimates of the treatment effect obtained using our methods to the benchmark results from the experiment. For both comparison groups, we show that the methods succeed in focusing attention on the small subset of the comparison units comparable to the treated units and, hence, in alleviating the bias due to systematic differences between the treated and comparison units. © 2002 by the President and Fellows of Harvard College and the Massachusetts Institute of Technology
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Bibliographic InfoArticle provided by MIT Press in its journal The Review of Economics and Statistics.
Volume (Year): 84 (2002)
Issue (Month): 1 (February)
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Other versions of this item:
- Rajeev H. Dehejia & Sadek Wahba, 1998. "Propensity Score Matching Methods for Non-experimental Causal Studies," NBER Working Papers 6829, National Bureau of Economic Research, Inc.
- Dehejia, R.H. & Wahba, S., 1998. "Propensity Score Matching Methods for Non-Experimental Causal Studies," Discussion Papers, Columbia University, Department of Economics 1998_02, Columbia University, Department of Economics.
- Rajeev H. Dehejia & Sadek Wahba, 2002. "Propensity score matching methods for non-experimental causal studies," Discussion Papers, Columbia University, Department of Economics 0102-14, Columbia University, Department of Economics.
- C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
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