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dc.contributor.authorFlores, Carlos A.en_US
dc.contributor.authorFlores-Lagunes, Alfonsoen_US
dc.date.accessioned2009-08-05en_US
dc.date.accessioned2010-07-07T11:55:30Z-
dc.date.available2010-07-07T11:55:30Z-
dc.date.issued2009en_US
dc.identifier.piurn:nbn:de:101:1-20090622213en_US
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_US
dc.language.isoengen_US
dc.publisherForschungsinstitut zur Zukunft der Arbeit GmbH Bonnen_US
dc.relation.ispartofseriesIZA Discussion Papers 4237en_US
dc.subject.jelC13en_US
dc.subject.jelC21en_US
dc.subject.jelC14en_US
dc.subject.ddc300en_US
dc.subject.keywordCausal inferenceen_US
dc.subject.keywordcausal mechanismsen_US
dc.subject.keywordpost-treatment variablesen_US
dc.subject.keywordprincipal stratificationen_US
dc.subject.stwStatistische Bestandsanalyseen_US
dc.subject.stwKausalanalyseen_US
dc.subject.stwDekompositionsverfahrenen_US
dc.subject.stwTheorieen_US
dc.subject.stwSch├Ątzungen_US
dc.subject.stwUSAen_US
dc.titleIdentification and estimation of causal mechanisms and net effects of a treatment under unconfoundednessen_US
dc.typeWorking Paperen_US
dc.identifier.ppn605236186en_US
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungen-
Appears in Collections:IZA Discussion Papers, Forschungsinstitut zur Zukunft der Arbeit (IZA)

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