Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/35701 
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dc.contributor.authorFlores, Carlos A.en
dc.contributor.authorFlores-Lagunes, Alfonsoen
dc.date.accessioned2009-08-05-
dc.date.accessioned2010-07-07T11:55:30Z-
dc.date.available2010-07-07T11:55:30Z-
dc.date.issued2009-
dc.identifier.piurn:nbn:de:101:1-20090622213en
dc.identifier.urihttp://hdl.handle.net/10419/35701-
dc.description.abstractAn important goal when analyzing the causal effect of a treatment on an outcome is to understand the mechanisms through which the treatment causally works. We define a causal mechanism effect of a treatment and the causal effect net of that mechanism using the potential outcomes framework. These effects provide an intuitive decomposition of the total effect that is useful for policy purposes. We offer identification conditions based on an unconfoundedness assumption to estimate them, within a heterogeneous effect environment, and for the cases of a randomly assigned treatment and when selection into the treatment is based on observables. Two empirical applications illustrate the concepts and methods.en
dc.language.isoengen
dc.publisher|aInstitute for the Study of Labor (IZA) |cBonnen
dc.relation.ispartofseries|aIZA Discussion Papers |x4237en
dc.subject.jelC13en
dc.subject.jelC21en
dc.subject.jelC14en
dc.subject.ddc300en
dc.subject.keywordCausal inferenceen
dc.subject.keywordcausal mechanismsen
dc.subject.keywordpost-treatment variablesen
dc.subject.keywordprincipal stratificationen
dc.subject.stwStatistische Bestandsanalyseen
dc.subject.stwKausalanalyseen
dc.subject.stwDekompositionsverfahrenen
dc.subject.stwTheorieen
dc.subject.stwSchätzungen
dc.subject.stwUSAen
dc.titleIdentification and estimation of causal mechanisms and net effects of a treatment under unconfoundedness-
dc.type|aWorking Paperen
dc.identifier.ppn605236186en
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen

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