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dc.contributor.authorLavado, Pablo
dc.contributor.authorRivera, Gonzalo
dc.identifier.citationLavado, P., & Rivera, G. (2015). Identifying treatment effects and counterfactual distributions using data combination with unobserved heterogeneity. Universidad del Pacífico, Centro de Investigación. Recuperado de
dc.description.abstractThis paper considers identification of treatment effects when the outcome variables and covari-ates are not observed in the same data sets. Ecological inference models, where aggregate out-come information is combined with individual demographic information, are a common example of these situations. In this context, the counterfactual distributions and the treatment effects are not point identified. However, recent results provide bounds to partially identify causal effects. Unlike previous works, this paper adopts the selection on unobservables assumption, which means that randomization of treatment assignments is not achieved until time fixed unobserved heterogeneity is controlled for. Panel data models linear in the unobserved components are con-sidered to achieve identification. To assess the performance of these bounds, this paper provides a simulation exercise.en
dc.publisherUniversidad del Pacífico. Centro de Investigaciónes_PE
dc.relation.ispartofseriesDocumento de discusión;DD1514
dc.rightsAtribución-NoComercial-SinDerivadas 4.0 Internacional*
dc.sourceRepositorio de la Universidad del Pacífico - UPes_PE
dc.sourceUniversidad del Pacíficoes_PE
dc.subjectVariables instrumentaleses_PE
dc.subjectDistribuciones contrafactualeses_PE
dc.titleIdentifying treatment effects and counterfactual distributions using data combination with unobserved heterogeneityes_PE

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