Welcome to the GitHub repository for a full-day short course providing a comprehensive introduction to the field of Targeted Learning, at the intersection of causal inference and machine learning, and its accompanying tlverse software ecosystem. Focus will be on targeted minimum loss estimators (TMLE) of causal effects, in particular of sophisticated intervention regimes (dynamic, optimal dynamic, stochastic), heterogeneous treatment effects, and ensemble machine learning of complex functionals (multinomial probabilities, conditional densities). The robust, efficient plug-in estimators that will be introduced leverage state-of-the-art, ensemble machine learning tools in order to flexibly adjust for confounding while yielding valid statistical inference. This course will be of interest to both statistical and applied scientists engaged in biomedical/health studies, whether experimental or observational, who wish to apply cutting-edge statistical and causal inference methodology to rigorously formalizing and answering substantive questions. This workshop incorporates interactive discussions and hands-on, guided Rprogramming exercises, allowing participants to familiarize themselves with methodology and tools that translate to real-world data analysis.
These teaching materials are adapted from a draft of the forthcoming
book Targeted Learning in R: Causal Data Science with the tlverse Software
Ecosystem, by Mark van der Laan, Jeremy
Coyle, Nima Hejazi, Ivana Malenica, Rachael Phillips, and Alan Hubbard. The
unabridged book is freely available; its
source materials may be found in the corresponding public GitHub
repository.
The workshop materials are automatically built and deployed using Binder, which supports using R and RStudio, with libraries pinned to a specific snapshot on MRAN. An RStudio session, pre-loaded with all the materials, is available via the "launch binder" button at the top of this page.