Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/34846 
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dc.contributor.authorMillimet, Daniel L.en
dc.contributor.authorTchernis, Rustyen
dc.date.accessioned2008-09-22-
dc.date.accessioned2010-07-07T11:30:02Z-
dc.date.available2010-07-07T11:30:02Z-
dc.date.issued2008-
dc.identifier.piurn:nbn:de:101:1-20080820174en
dc.identifier.urihttp://hdl.handle.net/10419/34846-
dc.description.abstractWe 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.en
dc.language.isoengen
dc.publisher|aInstitute for the Study of Labor (IZA) |cBonnen
dc.relation.ispartofseries|aIZA Discussion Papers |x3632en
dc.subject.jelC21en
dc.subject.ddc330en
dc.subject.keywordTreatment effectsen
dc.subject.keywordpropensity scoreen
dc.subject.keywordbiasen
dc.subject.keywordunconfoundednessen
dc.subject.keywordselection on unobservablesen
dc.subject.stwSchätztheorieen
dc.subject.stwBiasen
dc.subject.stwStichprobenverfahrenen
dc.subject.stwStatistischer Testen
dc.subject.stwSchätzungen
dc.subject.stwSchüleren
dc.subject.stwGemeinschaftsgastronomieen
dc.subject.stwGesundheitsrisikoen
dc.subject.stwUSAen
dc.titleMinimizing bias in selection on observables estimators when unconfoundness fails-
dc.type|aWorking Paperen
dc.identifier.ppn576953385en
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen

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