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Title:Minimizing bias in selection on observables estimators when unconfoundness fails PDF Logo
Authors:Millimet, Daniel L.
Tchernis, Rusty
Issue Date:2008
Series/Report no.:IZA Discussion Papers 3632
Abstract: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.
Subjects:Treatment effects
propensity score
bias
unconfoundedness
selection on unobservables
JEL:C21
Persistent Identifier of the first edition:urn:nbn:de:101:1-20080820174
Document Type:Working Paper
Appears in Collections:IZA Discussion Papers, Forschungsinstitut zur Zukunft der Arbeit (IZA)

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