Targeted Maximum Likelihood Estimation (Epidemiology)

Targeted maximum likelihood estimation (TMLE), introduced by Mark van der Laan and Daniel Rubin in 2006, is a doubly-robust, semiparametric framework for estimating causal effects that marries machine learning with the theory of efficient influence functions. It begins by flexibly estimating two nuisance quantities — the outcome regression and the propensity score — typically with an ensemble 'super learner,' and then performs a clever targeting step that nudges the outcome model in exactly the direction needed to remove plug-in bias for the causal parameter of interest. The result is a substitution estimator that is consistent if either the outcome model or the propensity model is correct (double robustness) and asymptotically efficient if both are, all while permitting aggressive data-adaptive estimation. Schuler and Rose's 2017 American Journal of Epidemiology tutorial brought TMLE to a broad epidemiologic audience, including social-epidemiologic applications where confounding structures are complex and functional forms unknown.