Identifying treatment effects and counterfactual distributions using data combination with unobserved heterogeneity

This 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....

Descripción completa

Guardado en:
Detalles Bibliográficos
Autores principales: Lavado, Pablo, Rivera, Gonzalo
Formato: Documento de trabajo
Lenguaje:Español
Publicado: Universidad del Pacífico. Centro de Investigación 2015
Materias:
Variables instrumentales
Distribuciones contrafactuales
Acceso en línea:http://hdl.handle.net/11354/1090
http://biblioteca.clacso.edu.ar/gsdl/cgi-bin/library.cgi?a=d&c=pe/pe-014&d=113541090oai
Aporte de:
Red de Bibliotecas Virtuales de Ciencias Sociales (CLACSO) de Consejo Latinoamericano de Ciencias Sociales
id I16-R122-113541090oai
record_format dspace
institution Consejo Latinoamericano de Ciencias Sociales
institution_str I-16
repository_str R-122
collection Red de Bibliotecas Virtuales de Ciencias Sociales (CLACSO)
language Español
topic Variables instrumentales
Distribuciones contrafactuales
spellingShingle Variables instrumentales
Distribuciones contrafactuales
Lavado, Pablo
Rivera, Gonzalo
Identifying treatment effects and counterfactual distributions using data combination with unobserved heterogeneity
topic_facet Variables instrumentales
Distribuciones contrafactuales
description This 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.
format Documento de trabajo
Documento de trabajo
author Lavado, Pablo
Rivera, Gonzalo
author_facet Lavado, Pablo
Rivera, Gonzalo
author_sort Lavado, Pablo
title Identifying treatment effects and counterfactual distributions using data combination with unobserved heterogeneity
title_short Identifying treatment effects and counterfactual distributions using data combination with unobserved heterogeneity
title_full Identifying treatment effects and counterfactual distributions using data combination with unobserved heterogeneity
title_fullStr Identifying treatment effects and counterfactual distributions using data combination with unobserved heterogeneity
title_full_unstemmed Identifying treatment effects and counterfactual distributions using data combination with unobserved heterogeneity
title_sort identifying treatment effects and counterfactual distributions using data combination with unobserved heterogeneity
publisher Universidad del Pacífico. Centro de Investigación
publishDate 2015
url http://hdl.handle.net/11354/1090
http://biblioteca.clacso.edu.ar/gsdl/cgi-bin/library.cgi?a=d&c=pe/pe-014&d=113541090oai
work_keys_str_mv AT lavadopablo identifyingtreatmenteffectsandcounterfactualdistributionsusingdatacombinationwithunobservedheterogeneity
AT riveragonzalo identifyingtreatmenteffectsandcounterfactualdistributionsusingdatacombinationwithunobservedheterogeneity
bdutipo_str Repositorios
_version_ 1764820417406566401

Ejemplares similares