# Welcome!

This open source, reproducible vignette is for a full-day workshop on the
Targeted Learning framework for statistical and causal inference with machine
learning, given at the ENAR 2021 Spring
Meeting on Sunday, 14 March 2021.
Beyond introducing Targeted Learning, the workshop focuses on applying the
methodology in practice using the `tlverse`

software
ecosystem. These materials are based on a working
draft of the book *Targeted Learning in R: Causal Data Science with the
tlverse Software Ecosystem*, which
includes in-depth discussion of these topics and much more, and may serve as a
useful reference to accompany 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 workshop at https://github.com/tlverse/enar2021-workshop/tree/master/R_code

## About this workshop

This full-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 dynamic,
optimal dynamic, and stochastic interventions. 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.

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 data analysis. 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 is useful but not
necessary. Familiarity with the `R`

programming language will be essential.

## Outline

- 08:00-09:00A: The Roadmap of Targeted Learning and Why We Need A Statistical
Revolution
with an
*introductory video lecture by Mark van der Laan and Alan Hubbard* - 09:00-09:30A: Morning Discussion
- 09:30-10:00A: Morning Coffee Break + Introductions
- 10:00-10:30A: Introduction to the
`tlverse`

Software Ecosystem and the WASH Benefits data - 10:30-11:30A: Super learning with the
`sl3`

`R`

package - 11:30A-12:30P: Programming exercises with
`sl3`

- 12:30-01:30P: Lunch Break with optional Q&A
- 01:30-02:30P: Targeted Learning for causal inference with the
`tmle3`

`R`

package - 02:30-03:30P: Programming exercises with
`tmle3`

- 03:30-03:45P: Afternoon Coffee Break
- 03:45-05:00P: Participants’ choice (by class vote), from among
- Optimal treatment regimes with the
`tmle3mopttx`

`R`

package - Stochastic treatment regimes with the
`tmle3shift`

`R`

package - Concluding review and discussion

- Optimal treatment regimes with the

**NOTE: All listings are in Eastern Time.**

## About the instructors

### Mark van der Laan

Mark van der Laan, PhD, is Professor of Biostatistics and Statistics at UC
Berkeley. His research interests include statistical methods in computational
biology, survival analysis, censored data, adaptive designs, targeted maximum
likelihood estimation, causal inference, data-adaptive loss-based learning, and
multiple testing. His research group developed loss-based super learning in
semiparametric models, based on cross-validation, as a generic optimal tool for
the estimation of infinite-dimensional parameters, such as nonparametric density
estimation and prediction with both censored and uncensored data. Building on
this work, his research group developed targeted maximum likelihood estimation
for a target parameter of the data-generating distribution in arbitrary
semiparametric and nonparametric models, as a generic optimal methodology for
statistical and causal inference. Most recently, Mark’s group has focused in
part on the development of a centralized, principled set of software tools for
targeted learning, the `tlverse`

.

### Alan Hubbard

Alan Hubbard is Professor of Biostatistics, former head of the Division of Biostatistics at UC Berkeley, and head of data analytics core at UC Berkeley’s SuperFund research program. His current research interests include causal inference, variable importance analysis, statistical machine learning, estimation of and inference for data-adaptive statistical target parameters, and targeted minimum loss-based estimation. Research in his group is generally motivated by applications to problems in computational biology, epidemiology, and precision medicine.

### Jeremy Coyle

Jeremy Coyle, PhD, is a consulting data scientist and statistical programmer,
currently leading the software development effort that has produced the
`tlverse`

ecosystem of R packages and related software tools. Jeremy earned his
PhD in Biostatistics from UC Berkeley in 2016, primarily under the supervision
of Alan Hubbard.

### Nima Hejazi

Nima Hejazi is a PhD candidate in biostatistics, working under the collaborative direction of Mark van der Laan and Alan Hubbard. Nima is affiliated with UC Berkeley’s Center for Computational Biology and NIH Biomedical Big Data training program, as well as with the Fred Hutchinson Cancer Research Center. Previously, he earned an MA in Biostatistics and a BA (with majors in Molecular and Cell Biology, Psychology, and Public Health), both at UC Berkeley. His research interests fall at the intersection of causal inference and machine learning, drawing on ideas from non/semi-parametric estimation in large, flexible statistical models to develop efficient and robust statistical procedures for evaluating complex target estimands in observational and randomized studies. Particular areas of current emphasis include mediation/path analysis, outcome-dependent sampling designs, targeted loss-based estimation, and vaccine efficacy trials. Nima is also passionate about statistical computing and open source software development for applied statistics.

### Ivana Malenica

Ivana Malenica is a PhD student in biostatistics advised by Mark van der Laan. Ivana is currently a fellow at the Berkeley Institute for Data Science, after serving as a NIH Biomedical Big Data and Freeport-McMoRan Genomic Engine fellow. She earned her Master’s in Biostatistics and Bachelor’s in Mathematics, and spent some time at the Translational Genomics Research Institute. Very broadly, her research interests span non/semi-parametric theory, probability theory, machine learning, causal inference and high-dimensional statistics. Most of her current work involves complex dependent settings (dependence through time and network) and adaptive sequential designs.

### Rachael Phillips

Rachael Phillips is a PhD student in biostatistics, advised by Alan Hubbard and Mark van der Laan. She has an MA in Biostatistics, BS in Biology, and BA in Mathematics. A student of targeted learning and causal inference; her research integrates personalized medicine, human-computer interaction, experimental design, and regulatory policy.

##
0.1 Reproduciblity with the `tlverse`

The `tlverse`

software ecosystem is a growing collection of packages, several of
which are quite early on in the software lifecycle. The team does its best to
maintain backwards compatibility. Once this work reaches completion, the
specific versions of the `tlverse`

packages used will be archived and tagged to
produce it.

This book was written using bookdown, and the complete source is available on GitHub. This version of the book was built with R version 4.0.4 (2021-02-15), pandoc version 2.7.3, and the following packages:

package | version | source |
---|---|---|

bookdown | 0.21.6 | Github (rstudio/bookdown@ed31991) |

bslib | 0.2.4.9002 | Github (rstudio/bslib@aa5a842) |

data.table | 1.14.0 | CRAN (R 4.0.4) |

delayed | 0.3.0 | Github (tlverse/delayed@47d90ae) |

devtools | 2.3.2 | CRAN (R 4.0.4) |

downlit | 0.2.1 | CRAN (R 4.0.4) |

dplyr | 1.0.5 | CRAN (R 4.0.4) |

ggplot2 | 3.3.3 | CRAN (R 4.0.4) |

here | 1.0.1 | CRAN (R 4.0.4) |

kableExtra | 1.3.4 | CRAN (R 4.0.4) |

knitr | 1.31 | CRAN (R 4.0.4) |

mvtnorm | 1.1-1 | CRAN (R 4.0.4) |

origami | 1.0.3 | CRAN (R 4.0.4) |

readr | 1.4.0 | CRAN (R 4.0.4) |

rmarkdown | 2.7.3 | Github (rstudio/rmarkdown@61db7a9) |

skimr | 2.1.3 | CRAN (R 4.0.4) |

sl3 | 1.4.3 | Github (tlverse/sl3@9b11f74) |

stringr | 1.4.0 | CRAN (R 4.0.4) |

tibble | 3.1.0 | CRAN (R 4.0.4) |

tidyr | 1.1.3 | CRAN (R 4.0.4) |

tidyverse | 1.3.0 | CRAN (R 4.0.4) |

tmle3 | 0.2.0 | Github (tlverse/tmle3@425e21c) |

tmle3mopttx | 0.1.0 | Github (tlverse/tmle3mopttx@5ba5f65) |

tmle3shift | 0.2.0 | Github (tlverse/tmle3shift@43f6fc0) |

## 0.2 Setup instructions

### 0.2.1 R and RStudio

**R** and **RStudio** are separate downloads and installations. R is the
underlying statistical computing environment. RStudio is a graphical integrated
development environment (IDE) that makes using R much easier and more
interactive. You need to install R before you install RStudio.

#### 0.2.1.1 Windows

##### 0.2.1.1.1 If you already have R and RStudio installed

- Open RStudio, and click on “Help” > “Check for updates”. If a new version is available, quit RStudio, and download the latest version for RStudio.
- To check which version of R you are using, start RStudio and the first thing
that appears in the console indicates the version of R you are
running. Alternatively, you can type
`sessionInfo()`

, which will also display which version of R you are running. Go on the CRAN website and check whether a more recent version is available. If so, please download and install it. You can check here for more information on how to remove old versions from your system if you wish to do so.

##### 0.2.1.1.2 If you don’t have R and RStudio installed

- Download R from the CRAN website.
- Run the
`.exe`

file that was just downloaded - Go to the RStudio download page
- Under
*Installers*select**RStudio x.yy.zzz - Windows XP/Vista/7/8**(where x, y, and z represent version numbers) - Double click the file to install it
- Once it’s installed, open RStudio to make sure it works and you don’t get any error messages.

#### 0.2.1.2 macOS / Mac OS X

##### 0.2.1.2.1 If you already have R and RStudio installed

- Open RStudio, and click on “Help” > “Check for updates”. If a new version is available, quit RStudio, and download the latest version for RStudio.
- To check the version of R you are using, start RStudio and the first thing
that appears on the terminal indicates the version of R you are running.
Alternatively, you can type
`sessionInfo()`

, which will also display which version of R you are running. Go on the CRAN website and check whether a more recent version is available. If so, please download and install it.

##### 0.2.1.2.2 If you don’t have R and RStudio installed

- Download R from the CRAN website.
- Select the
`.pkg`

file for the latest R version - Double click on the downloaded file to install R
- It is also a good idea to install XQuartz (needed by some packages)
- Go to the RStudio download page
- Under
*Installers*select**RStudio x.yy.zzz - Mac OS X 10.6+ (64-bit)**(where x, y, and z represent version numbers) - Double click the file to install RStudio
- Once it’s installed, open RStudio to make sure it works and you don’t get any error messages.

#### 0.2.1.3 Linux

- Follow the instructions for your distribution
from CRAN, they provide information
to get the most recent version of R for common distributions. For most
distributions, you could use your package manager (e.g., for Debian/Ubuntu run
`sudo apt-get install r-base`

, and for Fedora`sudo yum install R`

), but we don’t recommend this approach as the versions provided by this are usually out of date. In any case, make sure you have at least R 3.3.1. - Go to the RStudio download page
- Under
*Installers*select the version that matches your distribution, and install it with your preferred method (e.g., with Debian/Ubuntu`sudo dpkg -i rstudio-x.yy.zzz-amd64.deb`

at the terminal). - Once it’s installed, open RStudio to make sure it works and you don’t get any error messages.

These setup instructions are adapted from those written for Data Carpentry: R for Data Analysis and Visualization of Ecological Data.