Minimizing bias in selection on observables estimators when unconfoundness fails - Daniel L. Millimet; Rusty Tchernis (Institute for the Study of Labor (IZA), 2008)
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.
urn:nbn:de:101:1-20080820174
Keywords: C21; 330; Treatment effects; propensity score; bias; unconfoundedness; selection on unobservables; Schätztheorie; Bias; Stichprobenverfahren; Statistischer Test; Schätzung; Schüler; Gemeinschaftsgastronomie; Gesundheitsrisiko; USA