Dealing with Limited Overlap in Estimation of Average Treatment Effects

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Dealing with Limited Overlap in Estimation of Average Treatment Effects

Show simple item record Hotz, V. Joseph Crump, Richard K. Imbens, Guido Mitnik, Oscar A. 2009-05-28T15:40:04Z 2009
dc.identifier.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. en
dc.identifier.issn 0006-3444 en
dc.description.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]. en
dc.description.sponsorship Economics en
dc.language.iso en_US en
dc.publisher Oxford University Press en
dc.relation.isversionof en
dc.relation.hasversion en
dash.license OAP
dc.subject average treatment effect en
dc.subject causality en
dc.subject treatment effect heterogeneity en
dc.subject overlap en
dc.subject ignorable treatment assignment en
dc.subject propensity score en
dc.subject unconfoundedness en
dc.title Dealing with Limited Overlap in Estimation of Average Treatment Effects en
dc.relation.journal Biometrika en Imbens, Guido

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