eltmle: Ensemble Learning Targeted Maximum Likelihood Estimation

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Luque-Fernandez, MA. 2017. eltmle: Ensemble Learning Targeted Maximum Likelihood Estimation. [Online]. GitHub. Available from: : https://github.com/migariane/meltmle

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eltmle is a Stata program implementing the targeted maximum likelihood estimation for the ATE for a binary outcome and binary treatment. Future implementations will offer more general settings. eltmle includes the use of a "Super Learner" called from the SuperLearner package v.2.0-21 (Polley E., et al. 2011). The Super-Learner uses V-fold cross-validation (10-fold by default) to assess the performance of prediction regarding the potential outcomes and the propensity score as weighted averages of a set of machine learning algorithms. We used the default SuperLearner algorithms implemented in the base installation of the tmle-R package v.1.2.0-5 (Susan G. and Van der Laan M., 2017), which included the following: i) stepwise selection, ii) generalized linear modeling (glm), iii) a glm variant that included second order polynomials and two-by-two interactions of the main terms included in the model.

Published in a 3rd party system Date: 9 March 2017
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Data Creators(s): Luque-Fernandez, MA
LSHTM Faculty/Department: Faculty of Epidemiology and Population Health > Dept of Non-Communicable Disease Epidemiology
Participating Institutions: London School of Hygiene & Tropical Medicine


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  • GitHub (Online Data Resource)



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