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20082022
most citedComment: Performance of Double-Robust Estimators When ``Inverse Probability'' Weights Are Highly Variable

313 citations · 394 across the 7 of their papers we have counts for

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5 papers · 1 filter

stat.ME20201 cited

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…

stat.ME2019

Efficient estimation of optimal regimes under a no direct effect assumption

Lin Liu, Zach Shahn, James M. Robins +1

We derive new estimators of an optimal joint testing and treatment regime under the no direct effect (NDE) assumption that a given laboratory, diagnostic, or screening test has no…

stat.ME201719 cited

On the multiply robust estimation of the mean of the g-functional

Andrea Rotnitzky, James Robins, Lucia Babino

We study multiply robust (MR) estimators of the longitudinal g-computation formula of Robins (1986). In the first part of this paper we review and extend the recently proposed para…

stat.ME201520 cited

Causal Etiology of the Research of James M. Robins

Thomas S. Richardson, Andrea Rotnitzky

This issue of Statistical Science draws its inspiration from the work of James M. Robins. Jon Wellner, the Editor at the time, asked the two of us to edit a special issue that woul…

stat.ME2008313 cited

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]