Lucia Babino
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Lucia Babino has a total of 2 co-authors. Here are the top co-authors by number of shared publications:
- Andrea Rotnitzky
Department of Economics Universidad Torcuato Di Tella Buenos Aires Argentina
1 shared publication - James M. Robins
Harvard School of Public Health Cambridge 02115 MA USA
1 shared publication
Latest Publications
- 13 July 2018
- Published by Oxford University Press (OUP) in Biometrics
- Vol. 75 (1) , 90-99
Multiple Robust Estimation of Marginal Structural Mean Models for Unconstrained Outcomes
Summary: We consider estimation, from longitudinal observational data, of the parameters of marginal structural mean models for unconstrained outcomes. Current proposals include inverse probability of treatment weighted and double robust (DR) estimators. A difficulty with DR estimation is that it requires postulating a sequence of models, one for the each mean of the counterfactual outcome given covariate and treatment history up to each exposure time point. Most natural models for such means are often incompatible. Robins et al., (2000b) proposed a parameterization of the likelihood which implies compatible parametric models for such means. Their parameterization has not been exploited to construct DR estimators and one goal of this article is to fill this gap. More importantly, exploiting this parameterization we propose a multiple robust (MR) estimator that confers even more protection against model misspecification than DR estimators. Our methods are easy to implement as they are based on the iterative fit of a sequence of weighted regressions.
- 13 July 2018
- Published by Oxford University Press (OUP) in Biometrics
- Vol. 75 (1) , 90-99
Multiple Robust Estimation of Marginal Structural Mean Models for Unconstrained Outcomes
Summary: We consider estimation, from longitudinal observational data, of the parameters of marginal structural mean models for unconstrained outcomes. Current proposals include inverse probability of treatment weighted and double robust (DR) estimators. A difficulty with DR estimation is that it requires postulating a sequence of models, one for the each mean of the counterfactual outcome given covariate and treatment history up to each exposure time point. Most natural models for such means are often incompatible. Robins et al., (2000b) proposed a parameterization of the likelihood which implies compatible parametric models for such means. Their parameterization has not been exploited to construct DR estimators and one goal of this article is to fill this gap. More importantly, exploiting this parameterization we propose a multiple robust (MR) estimator that confers even more protection against model misspecification than DR estimators. Our methods are easy to implement as they are based on the iterative fit of a sequence of weighted regressions.