ELTMLE: Stata module to provide Ensemble Learning Targeted Maximum Likelihood Estimation
Miguel Angel Luque-Fernandez ()
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Miguel Angel Luque-Fernandez: LSHTM, NCDE, Cancer Survival Group, London, UK
Statistical Software Components from Boston College Department of Economics
eltmle is a Stata program implementing the targeted maximum likelihood estimation for the average treatment effect for a binary outcome and binary treatment. eltmle includes the use of a super-learner called from the SuperLearner package v.2.0-21 in R (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 includes second order polynomials and two-by-two interactions of the main terms included in the model. Additionally, eltmle users will have the option to include Bayes Generalized Linear Models and Generalized Additive Models as additional Super-Learner algorithms. Future implementations will offer more advanced machine learning algorithms.
Requires: Stata version 13.2 and R
Keywords: maximum likelihood; machine learning; binary outcome; binary treatment (search for similar items in EconPapers)
Date: 2017-04-14, Revised 2018-10-18
Note: This module should be installed from within Stata by typing "ssc install eltmle". Windows users should not attempt to download these files with a web browser.
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http://fmwww.bc.edu/repec/bocode/e/eltmle.ado program code (text/plain)
http://fmwww.bc.edu/repec/bocode/e/eltmle.sthlp help file (text/plain)
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Persistent link: https://EconPapers.repec.org/RePEc:boc:bocode:s458337
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