Treatment Effects With Unobserved Heterogeneity: A Set Identification Approach

Jun, SJ; Lee, Y; Shin, Y

Treatment Effects With Unobserved Heterogeneity: A Set Identification Approach

Jun, SJ Lee, Y Shin, Y

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Publication Type:: Journal Article
Citation:: Journal of Business and Economic Statistics, 2016, 34 (2), pp. 302 - 311
Issue Date:: 2016-04-02

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Field	Value	Language
dc.contributor.author	Jun, SJ	en_US
dc.contributor.author	Lee, Y	en_US
dc.contributor.author	Shin, Y https://orcid.org/0000-0002-2711-6906	en_US
dc.date.issued	2016-04-02	en_US
dc.identifier.citation	Journal of Business and Economic Statistics, 2016, 34 (2), pp. 302 - 311	en_US
dc.identifier.issn	0735-0015	en_US
dc.identifier.uri	http://hdl.handle.net/10453/121924
dc.description.abstract	© 2016 American Statistical Association. We propose the sharp identifiable bounds of the potential outcome distributions using panel data. We allow for the possibility that statistical randomization of treatment assignments is not achieved until unobserved heterogeneity is properly controlled for. We use certain stationarity assumptions to obtain the sharp bounds. Our approach allows for dynamic treatment decisions, where the current treatment decisions may depend on the past treatments or the past observed outcomes. As an empirical illustration, we study the effect of smoking during pregnancy on infant birthweight. We find that for the group of switchers the infant birthweight of a smoking mother is first-order stochastically dominated by that of a nonsmoking mother.	en_US
dc.relation.ispartof	Journal of Business and Economic Statistics	en_US
dc.relation.isbasedon	10.1080/07350015.2015.1044008	en_US
dc.subject.classification	Econometrics	en_US
dc.title	Treatment Effects With Unobserved Heterogeneity: A Set Identification Approach	en_US
dc.type	Journal Article
utslib.citation.volume	2	en_US
utslib.citation.volume	34	en_US
utslib.for	1403 Econometrics	en_US
utslib.for	01 Mathematical Sciences	en_US
utslib.for	14 Economics	en_US
utslib.for	15 Commerce, Management, Tourism and Services	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Business
utslib.copyright.status	closed_access
pubs.issue	2	en_US
pubs.publication-status	Published	en_US
pubs.volume	34	en_US

Abstract:

© 2016 American Statistical Association. We propose the sharp identifiable bounds of the potential outcome distributions using panel data. We allow for the possibility that statistical randomization of treatment assignments is not achieved until unobserved heterogeneity is properly controlled for. We use certain stationarity assumptions to obtain the sharp bounds. Our approach allows for dynamic treatment decisions, where the current treatment decisions may depend on the past treatments or the past observed outcomes. As an empirical illustration, we study the effect of smoking during pregnancy on infant birthweight. We find that for the group of switchers the infant birthweight of a smoking mother is first-order stochastically dominated by that of a nonsmoking mother.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/121924