Minimizing Bias in Selection on Observables Estimators When Unconfoundness Fails
نویسندگان
چکیده
Minimizing Bias in Selection on Observables Estimators When Unconfoundness Fails We characterize the bias of propensity score based estimators of common average treatment effect parameters in the case of selection on unobservables. We then propose a new minimum biased estimator of the average treatment effect. We assess the finite sample performance of our estimator using simulated data, as well as a timely application examining the causal effect of the School Breakfast Program on childhood obesity. We find our new estimator to be quite advantageous in many situations, even when selection is only on observables. JEL Classification: C21, C52
منابع مشابه
Estimation of Treatment E¤ects Without an Exclusion Restriction: with an Application to the Analysis of the School Breakfast Program
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