eltmle is a Stata program implementing the targeted maximum likelihood estimation (TMLE) for the ATE for a binary or continuous outcome and binary treatment. eltmle includes the use of a super-learner called from the SuperLearner package v.2.0-21 (Polley E., et al. 2011). Modern Epidemiology has been able to identify significant limitations of classic epidemiological methods, like outcome regression analysis, when estimating causal quantities such as the average treatment effect (ATE) for observational data. For example, using classical regression models to estimate the ATE requires making the assumption that the effect measure is constant across levels of confounders included in the model, i.e. that there is no effect modification. Other methods do not require this assumption, including g-methods (e.g. the g-formula) and targeted maximum likelihood estimation (TMLE). The average treatment effect (ATE) or risk difference is the most commonly used causal parameter. Many estimators of the ATE but no all rely on parametric modeling assumptions. Therefore, the correct model specification is crucial to obtain unbiased estimates of the true ATE. TMLE is a semi-parametric, efficient substitution estimator allowing for data-adaptive estimation while obtaining valid statistical inference based on the targeted minimum loss-based estimation. TMLE has the advantage of being doubly robust. Moreover, TMLE allows inclusion of machine learning algorithms to minimise the risk of model misspecification, a problem that persists for competing estimators. Evidence shows that TMLE typically provides the least unbiased estimates of the ATE compared with other double robust estimators. The following links provides access to a TMLE tutorial: https://migariane.github.io/TMLE.nb.html and the GitHub repository for eltmle Stata package: https://github.com/migariane/eltmle