TY  - RPRT
        AU  - Millimet, Daniel L.
AU  - Tchernis, Rusty
        TI  - Minimizing Bias in Selection on Observables Estimators When Unconfoundness Fails
        PY  - 2008/Aug/
        PB  - Institute of Labor Economics (IZA)
        CY  - Bonn
        T2  - IZA Discussion Paper
        IS  - 3632
        UR  - https://www.iza.org/publications/dp3632
        AB  - 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.
        KW  - selection on unobservables
KW  - unconfoundedness
KW  - treatment effects
KW  - propensity score
KW  - bias
        ER  -