Models for the propensity score that contemplate the positivity assumption and their application to missing data and causality
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 literatu...
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John Wiley and Sons Ltd
2018
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| 100 | 1 | |a Molina, J. | |
| 245 | 1 | 0 | |a Models for the propensity score that contemplate the positivity assumption and their application to missing data and causality |
| 260 | |b John Wiley and Sons Ltd |c 2018 | ||
| 270 | 1 | 0 | |m Valdora, M.; Universidad de Buenos Aires, Facultad de Ciencias Exactas y Naturales, Instituto de CálculoArgentina; email: mvaldora@dm.uba.ar |
| 506 | |2 openaire |e Política editorial | ||
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| 520 | 3 | |a 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. Copyright © 2018 John Wiley & Sons, Ltd. |l eng | |
| 536 | |a Detalles de la financiación: Universidad de Buenos Aires | ||
| 536 | |a Detalles de la financiación: This research was partly supported by grants 20020150200110BA and 20020130100279BA from Universidad de Buenos Aires. | ||
| 593 | |a Universidad de Buenos Aires, Ciclo Básico Común, Buenos Aires, Argentina | ||
| 593 | |a Consejo Nacional de Investigaciones Científicas y Técnicas, Buenos Aires, Argentina | ||
| 593 | |a Universidad de Buenos Aires, Facultad de Ciencias Exactas y Naturales, Instituto de Cálculo, Buenos Aires, Argentina | ||
| 690 | 1 | 0 | |a AVERAGE TREATMENT EFFECT |
| 690 | 1 | 0 | |a INVERSE PROBABILITY WEIGHTING |
| 690 | 1 | 0 | |a MISSING DATA |
| 690 | 1 | 0 | |a OBSERVATIONAL STUDIES |
| 690 | 1 | 0 | |a POSITIVITY |
| 690 | 1 | 0 | |a ARTICLE |
| 690 | 1 | 0 | |a OBSERVATIONAL STUDY |
| 690 | 1 | 0 | |a PROBABILITY |
| 690 | 1 | 0 | |a PROPENSITY SCORE |
| 690 | 1 | 0 | |a THEORETICAL STUDY |
| 700 | 1 | |a Sued, M. | |
| 700 | 1 | |a Valdora, M. | |
| 773 | 0 | |d John Wiley and Sons Ltd, 2018 |g v. 37 |h pp. 3503-3518 |k n. 24 |p Stat. Med. |x 02776715 |t Statistics in Medicine | |
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| 856 | 4 | 0 | |u https://doi.org/10.1002/sim.7827 |y DOI |
| 856 | 4 | 0 | |u https://hdl.handle.net/20.500.12110/paper_02776715_v37_n24_p3503_Molina |y Handle |
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