Dealing with Limited Overlap in Estimation of Average Treatment Effects

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Dealing with Limited Overlap in Estimation of Average Treatment Effects

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Title: Dealing with Limited Overlap in Estimation of Average Treatment Effects
Author: Hotz, V. Joseph; Crump, Richard K.; Imbens, Guido; Mitnik, Oscar A.

Note: Order does not necessarily reflect citation order of authors.

Citation: Crump, Richard K., V. Joseph Hotz, Guido W. Imbens, and Oscar A. Mitnik. 2009. Dealing with limited overlap in estimation of average treatment effects. Biometrika 96(1): 187-199.
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Abstract: Estimation of average treatment effects under unconfounded or ignorable treatment assignment is often hampered by lack of overlap in the covariate distributions between treatment groups. 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 ad hoc methods for trimming the sample. We develop a systematic approach to addressing lack of overlap. We characterize optimal subsamples for which the average treatment effect can be estimated most precisely. 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 rule of thumb to discard all units with estimated propensity scores outside the range [0.1,0.9].
Published Version: http://dx.doi.org/10.1093/biomet/asn055
Other Sources: http://ssrn.com/abstract=937912
Terms of Use: This article is made available under the terms and conditions applicable to Open Access Policy Articles, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#OAP
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:3007645

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  • FAS Scholarly Articles [7103]
    Peer reviewed scholarly articles from the Faculty of Arts and Sciences of Harvard University
 
 

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