Mean-Square-Error Calculations for Average Treatment Effects

This paper develops a new efficient estimator for the average treatment effect, if selection for treatment is on observables. The new estimator is linear in the first-stage nonparametric estimator. This simplifies the derivation of the means squared error (MSE) of the estimator as a function of the number of basis functions that is used in the first stage nonparametric regression. We propose an estimator for the MSE and show that in large samples minimization of this estimator is equivalent to minimization of the population MSE.

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