Richard K. Crump () (Department of Economics, University of California at Berekely) V. Joseph Hotz () (Department of Economics, University of California at Lost Angeles) Guido W. Imbens () (Department of Economics, Harvard University) Oscar A. Mitnik () (Department of Economics, University of Miami)
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Estimation of average treatment effects under unconfounded or ignorable 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 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 University of Miami, Department of Economics in its series Working Papers with number
0716.
Find related papers by JEL classification: C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Semiparametric and Nonparametric Methods C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation and Testing
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