Targeted maximum likelihood estimation for a binary treatment: A tutorial

Contrastingly, propensity score methods require the correct specification of an exposure model. Double-robust methods only require correct specification of one of these models.

TMLE is a semi-parametric double-robust method that improves the chances of correct model specification by allowing for flexible estimation using non-parametric machine-learning methods. It therefore requires weaker assumptions than its competitors. We provide a step-by-step guided implementation of TMLE and illustrate it in a realistic scenario based on cancer epidemiology where assumptions about correct model specification and positivity (i.e., when a study participant had zero probability of receiving the treatment) are nearly violated.