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

Understand

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.