# Targeted Learning with the `tlverse`

Software Ecosystem

*Workshop, 20 February 2020, Conference on Statistical Practice*

*updated: February 20, 2020*

# Preface

This is an open source and fully-reproducible electronic vignette for an
invited short-course on applying the targeted learning methodology in practice
using the `tlverse`

software ecosystem, given at
the Conference on Statistical Practice (CSP) on 20 February 2020. *The
Hitchhiker’s Guide to the tlverse, or a Targeted Learning Practitioner’s
Handbook* is an in-draft book covering
the same topics in greater detail and may serve as a useful accompanying
resource to these workshop materials.

## Important links

**Software installation**: Please install the relevant software before the workshop using the installation script.You will probably exceed the GitHub API rate limit during this installation, which will throw an error. This issue and the solution are addressed here.

**Code**:`R`

script files for each section of the workshop are available via the GitHub repository for the short course at https://github.com/tlverse/csp2020-workshop/tree/master/R

## About this workshop

This 1-day workshop will provide a comprehensive introduction to the field of
*Targeted Learning* and the corresponding `tlverse`

software
ecosystem. In particular, we will focus on
targeted minimum loss estimators of causal effects, including those of static
and dynamic treatments (as well as those of optimal dynamic and stochastic
interventions, time permitting). These multiply robust, efficient plug-in
estimators use state-of-the-art, ensemble machine learning tools to flexibly
adjust for confounding while yielding valid statistical inference. We will
discuss the utility of this robust estimation strategy in comparison to
conventional techniques, which often rely on restrictive statistical models and
may therefore lead to severely biased inference. In addition to discussion, this
workshop will incorporate both interactive activities and hands-on, guided `R`

programming exercises, to allow participants the opportunity to familiarize
themselves with methodology and tools that will translate to real-world causal
inference analyses. It is highly recommended for participants to have an
understanding of basic statistical concepts such as confounding, probability
distributions, confidence intervals, hypothesis tests, and regression. Advanced
knowledge of mathematical statistics may be useful but is not necessary.
Familiarity with the `R`

programming language will be essential.

## Outline

- 8:00-10:00A: Introductory Presentation and Discussion
- 10:00-10:15A: Break
- 10:15A-10:45A: Introduction to the
`tlverse`

- 10:45A-11:15A: Introduction to
`R6`

- 11:15A-11:30P: Overview of Example Datasets
- 11:30A-12:00P:
`tlverse`

Software Installation - 12:00P-1:00P: Lunch
- 1:00-3:00P: Super (Machine) Learning with the
`sl3`

`R`

package - 3:00-3:15P: Break
- 03:15-05:00P: Targeted Maximum Likelihood Estimation (TMLE) with the
`tmle3`

`R`

package