Canonical Least Favorable Submodels:A New TMLE Procedure for Multidimensional Parameters

TL;DR

This paper introduces a new canonical least favorable submodel for TMLE that simplifies the targeting step for multidimensional parameters, potentially improving efficiency and stability in high-dimensional settings.

Contribution

It defines a single-dimensional submodel for multidimensional parameters, simplifying the iterative TMLE process and addressing issues related to high-dimensional parameters and positivity violations.

Findings

Introduces a canonical least favorable submodel using a single epsilon.

Enables weighting via inverse weights in an offset intercept model.

Implemented in several software packages for practical use.

Abstract

This paper is a fundamental addition to the world of targeted maximum likelihood estimation (TMLE) (or likewise, targeted minimum loss estimation) for simultaneous estimation of multi-dimensional parameters of interest. TMLE, as part of the targeted learning framework, offers a crucial step in constructing efficient plug-in estimators for nonparametric or semiparametric models. The so-called targeting step of targeted learning, involves fluctuating the initial fit of the model in a way that maximally adjusts the plug-in estimate per change in the log likelihood. Previously for multidimensional parameters of interest, iterative TMLE's were constructed using locally least favorable submodels as defined in van der Laan and Gruber, 2016, which are indexed by a multidimensional fluctuation parameter. In this paper we define a canonical least favorable submodel in terms of a single dimensional epsilon for a $d$-dimensional parameter of interest. One can view the clfm as the iterative analog to the one-step TMLE as constructed in van der Laan and Gruber, 2016. It is currently implemented in several software packages we provide in the last section. Using a single epsilon for the targeting step in TMLE could be useful for high dimensional parameters, where using a fluctuation parameter of the same dimension as the parameter of interest could suffer the consequences of curse of dimensionality. The clfm also enables placing the so-called clever covariate denominator as an inverse weight in an offset intercept model. It has been shown that such weighting mitigates the effect of large inverse weights sometimes caused by near positivity violations.