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
0330.
Length: Date of creation: Oct 2006 Date of revision: Handle: RePEc:nbr:nberte:0330
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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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