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

By Andrea Rotnitzky, James M. Robins, Lucia Babino. 2017

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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 parametric multiply robust estimators of Tchetgen-Tchetgen (2009) and Molina, Rotnitzky, Sued and Robins (2017). In the second part of the paper we derive multiply and doubly robust estimators that use non-parametric machine-learning (ML) estimators of nuisance functions in lieu of parametric models. We use sample splitting to avoid the need for Donsker conditions, thereby allowing an analyst to select the ML algorithms of their choosing. We contrast the asymptotic behavior of

Subfields: Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/3c4e20de13fea4d3db51fdb35ed3e7cd809412b3