The Extensible TMLE framework
Author: Jeremy Coyle
tmle3 is a general framework that supports the implementation of a
range of Targeted Maximum Likelihood / Minimum Loss-Based Estimation
(TMLE) parameters through exposing a unified interface. The goal is that
tmle3 framework be as general as the mathematical framework upon
which it’s based.
For a general discussion of the framework of targeted minimum loss-based estimation and the role this methodology plays in statistical and causal inference, the canonical references are van der Laan and Rose (2011) and van der Laan and Rose (2018).
You can install the development version of
tmle3 from GitHub via
The best place to get started is the “Framework Overview” document,
which describes the individual components of the
tmle3 framework. It
may be found at https://tlverse.org/tmle3/articles/framework.html.
If you encounter any bugs or have any specific feature requests, please file an issue.
© 2017-2019 Jeremy R. Coyle
The contents of this repository are distributed under the GPL-3 license.
LICENSE for details.
van der Laan, Mark J, and Sherri Rose. 2011. Targeted Learning: Causal Inference for Observational and Experimental Data. Springer Science & Business Media.
———. 2018. Targeted Learning in Data Science: Causal Inference for Complex Longitudinal Studies. Springer Science & Business Media.