313 citations · 414 across the 14 of their papers we have counts for
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Asymptotic results for nonparametric regression estimators after sufficient dimension reduction estimation
Liliana Forzani, Daniela Rodriguez, Mariela Sued
Prediction, in regression and classification, is one of the main aims in modern data science. When the number of predictors is large, a common first step is to reduce the dimension…
Double-robust and efficient methods for estimating the causal effects of a binary treatment
James Robins, Mariela Sued, Quanhong Lei-Gomez +1
We consider the problem of estimating the effects of a binary treatment on a continuous outcome of interest from observational data in the absence of confounding by unmeasured fact…
Robust Doubly Protected Estimators for Quantiles with Missing Data
Julieta Molina, Mariela Sued, Marina Valdora +1
Doubly protected estimators are widely used for estimating the population mean of an outcome Y from a sample where the response is missing in some individuals. To compensate for th…
Models for the Propensity Score that Contemplate the Positivity Assumption and their Application to Missing Data and Causality
Julieta Molina, Mariela Sued, Marina Valdora
Generalized linear models are often assumed to fit propensity scores, which are used to compute inverse probability weighted (IPW) estimators. In order to derive the asymptotic pro…
Comment: Performance of Double-Robust Estimators When ``Inverse Probability'' Weights Are Highly Variable
James Robins, Mariela Sued, Quanhong Lei-Gomez +1
Comment on ``Performance of Double-Robust Estimators When ``Inverse Probability'' Weights Are Highly Variable'' [arXiv:0804.2958]