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Moving the Goalposts: Addressing Limited Overlap in the Estimation of Average Treatment Effects by Changing the Estimand Author info | Abstract | Publisher info | Download info | Related research | Statistics Richard K. Crump
V. Joseph Hotz
Guido W. Imbens
Oscar A. Mitnik
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Estimation of average treatment effects under unconfoundedness or exogenous treatment assignment is often hampered by lack of overlap in the covariate distributions. This lack of overlap can lead to imprecise estimates and can make commonly used estimators sensitive to the choice of specification. In such cases researchers have often used informal methods for trimming the sample. In this paper we develop a systematic approach to addressing such lack of overlap. We characterize optimal subsamples for which the average treatment effect can be estimated most precisely, as well as optimally weighted average treatment effects. Under some conditions the optimal selection rules depend solely on the propensity score. For a wide range of distributions a good approximation to the optimal rule is provided by the simple selection rule to drop all units with estimated propensity scores outside the range [0.1,0.9].
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Paper provided by National Bureau of Economic Research, Inc in its series NBER Technical Working Papers with number
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Date of creation: Oct 2006Date of revision:
Handle: RePEc:nbr:nberte:0330Note: TWPContact details of provider: Postal: National Bureau of Economic Research, 1050 Massachusetts Avenue Cambridge, MA 02138, U.S.A. Phone: 617-868-3900 Email: Web page: http://www.nber.org More information through EDIRC
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Find related papers by JEL classification: C01 - Mathematical and Quantitative Methods - - General - - - Econometrics C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Estimation C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Semiparametric and Nonparametric Methods C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
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"On the Role of the Propensity Score in Efficient Semiparametric Estimation of Average Treatment Effects ,"
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Full
references Cited by : (explanations , Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile , click on "citations" and make appropriate adjustments.)
Richard K. Crump & V. Joseph Hotz & Guido W. Imbens & Oscar A. Mitnik, 2006.
"Nonparametric Tests for Treatment Effect Heterogeneity ,"
NBER Technical Working Papers
0324, National Bureau of Economic Research, Inc.
[Downloadable!] (restricted)
Other versions:
Richard K. Crump & V. Joseph Hotz & Guido W. Imbens & Oscar A. Mitnik, .
"Nonparametric Tests for Treatment Effect Heterogeneity ,"
Working Papers
0609, University of Miami, Department of Economics.
[Downloadable!] Richard K. Crump & V. Joseph Hotz & Guido W. Imbens & Oscar A. Mitnik, 2006.
"Nonparametric Tests for Treatment Effect Heterogeneity ,"
IZA Discussion Papers
2091, Institute for the Study of Labor (IZA).
[Downloadable!] Richard K. Crump & V. Joseph Hotz & Guido W. Imbens & Oscar A. Mitnik, 2008.
"Nonparametric Tests for Treatment Effect Heterogeneity ,"
The Review of Economics and Statistics ,
MIT Press, vol. 90(3), pages 389-405, 06.
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