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dc.contributor.author
Molina, Julieta
dc.contributor.author
Sued, Raquel Mariela
dc.contributor.author
Valdora, M.
dc.date.available
2019-12-19T19:35:09Z
dc.date.issued
2018-06
dc.identifier.citation
Molina, Julieta; Sued, Raquel Mariela; Valdora, M.; Models for the propensity score that contemplate the positivity assumption and their application to missing data and causality; John Wiley & Sons Ltd; Statistics In Medicine; 37; 24; 6-2018; 3503-3518
dc.identifier.issn
0277-6715
dc.identifier.uri
http://hdl.handle.net/11336/92574
dc.description.abstract
Generalized linear models are often assumed to fit propensity scores, which are used to compute inverse probability weighted (IPW) estimators. To derive the asymptotic properties of IPW estimators, the propensity score is supposed to be bounded away from zero. This condition is known in the literature as strict positivity (or positivity assumption), and, in practice, when it does not hold, IPW estimators are very unstable and have a large variability. Although strict positivity is often assumed, it is not upheld when some of the covariates are unbounded. In real data sets, a data-generating process that violates the positivity assumption may lead to wrong inference because of the inaccuracy in the estimations. In this work, we attempt to conciliate between the strict positivity condition and the theory of generalized linear models by incorporating an extra parameter, which results in an explicit lower bound for the propensity score. An additional parameter is added to fulfil the overlap assumption in the causal framework.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
John Wiley & Sons Ltd
dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
AVERAGE TREATMENT EFFECT
dc.subject
INVERSE PROBABILITY WEIGHTING
dc.subject
MISSING DATA
dc.subject
OBSERVATIONAL STUDIES
dc.subject
POSITIVITY
dc.subject.classification
Estadística y Probabilidad
dc.subject.classification
Matemáticas
dc.subject.classification
CIENCIAS NATURALES Y EXACTAS
dc.title
Models for the propensity score that contemplate the positivity assumption and their application to missing data and causality
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2019-12-16T19:11:13Z
dc.journal.volume
37
dc.journal.number
24
dc.journal.pagination
3503-3518
dc.journal.pais
Reino Unido
dc.journal.ciudad
Londres
dc.description.fil
Fil: Molina, Julieta. Universidad de Buenos Aires; Argentina
dc.description.fil
Fil: Sued, Raquel Mariela. Universidad de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
dc.description.fil
Fil: Valdora, M.. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Cálculo; Argentina
dc.journal.title
Statistics In Medicine
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://onlinelibrary.wiley.com/doi/abs/10.1002/sim.7827
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1002/sim.7827
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