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[ACIC 2022] Targeted Learning in the tlverse: Causal Inference Meets Machine Learning

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. 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.


About this workshop

This workshop will provide a comprehensive overview to the field of Targeted Learning for causal inference, and the corresponding tlverse software ecosystem, with a particular focus placed on sophisticated intervention regimes (dynamic, optimal dynamic, stochastic). Emphasis will be placed on targeted minimum loss-based estimators of the causal effects of single timepoint interventions, including extensions for missing covariate and outcome data. 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 testing, and regression. Advanced knowledge of mathematical statistics is useful but not necessary. Familiarity with the R programming language will be essential.

Outline

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, PhD, is an incoming Assistant Professor of Biostatistics at the Harvard T.H. Chan School of Public Health. He received his PhD in biostatistics at UC Berkeley, working under the supervision of Mark van der Laan and Alan Hubbard, and afterwards held an NSF postdoctoral research fellowship. Nima’s research interests blend causal inference, machine learning, semiparametric estimation, and computational statistics – areas of recent emphasis include causal mediation analysis, efficiency under biased sampling designs, non/semi-parametric sieve estimation with machine learning, and targeted loss-based estimation. His work is primarily driven by applications in clinical trials (esp. vaccine efficacy trials), infectious disease epidemiology, and computational biology. Nima is passionate about statistical computing and open source software design standards for statistical data science, and he has co-led or contributed significantly to many tlverse packages (hal9001, sl3, tmle3, origami, tmle3shift, tmle3mediate).

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, Rachael’s research integrates semiparametric statistical estimation and inference. She is motivated by applied projects and some of her current work involves personalized online learning from data streams of vital signs, human-computer interaction, automated machine learning, and developing statistical analysis plans using targeted learning.

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 4.2.0 (2022-04-22), pandoc version 2.7.3, and the following packages:

package version source
bookdown 0.26.3 Github (rstudio/bookdown@169c43b6bb95213f2af63a95acd4e977a58a3e1f)
bslib 0.3.1.9000 Github (rstudio/bslib@a4946a49499438e71dce29c810a41e2d05170376)
data.table 1.14.2 CRAN (R 4.2.0)
delayed 0.4.0 Github (tlverse/delayed@bf7ca82ff316f22e84706e9128e8d832be0785bc)
devtools 2.4.3 CRAN (R 4.2.0)
downlit 0.4.0 CRAN (R 4.2.0)
dplyr 1.0.9 CRAN (R 4.2.0)
future 1.26.1 CRAN (R 4.2.0)
ggplot2 3.3.6 CRAN (R 4.2.0)
here 1.0.1 CRAN (R 4.2.0)
kableExtra 1.3.4 CRAN (R 4.2.0)
knitr 1.39 CRAN (R 4.2.0)
mvtnorm 1.1-3 CRAN (R 4.2.0)
origami 1.0.5 Github (tlverse/origami@e1b8fe6f5e75fff1d48eed115bb81475c9bd506e)
readr 2.1.2 CRAN (R 4.2.0)
rmarkdown 2.14 CRAN (R 4.2.0)
skimr 2.1.4 CRAN (R 4.2.0)
sl3 1.4.5 Github (tlverse/sl3@de445c210eefa5aa9dd4c0d1fab8126f0d7c5eeb)
stringr 1.4.0 CRAN (R 4.2.0)
tibble 3.1.7 CRAN (R 4.2.0)
tidyr 1.2.0 CRAN (R 4.2.0)
tidyverse 1.3.1 CRAN (R 4.2.0)
tmle3 0.2.0 Github (tlverse/tmle3@ed72f8a20e64c914ab25ffe015d865f7a9963d27)
tmle3mediate 0.0.3 Github (tlverse/tmle3mediate@70d1151c4adb54d044f355d06d07bcaeb7f8ae07)
tmle3mopttx 1.0.0 Github (tlverse/tmle3mopttx@c8c675f051bc5ee6d51fa535fe6dc80791d4d1b7)
tmle3shift 0.2.0 Github (tlverse/tmle3shift@4ed52b50af501a5fa2e6257b568d17fd485d3f42)

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.