# 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.

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.

## About the instructors and authors

### Mark van der Laan

Mark van der Laan, Ph.D., 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. For more information, see https://vanderlaan-lab.org.

### Jeremy Coyle

Jeremy Coyle, Ph.D., 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 Ph.D. in Biostatistics from UC Berkeley in 2016, primarily under the supervision of Alan Hubbard.

### Alan Hubbard

Alan Hubbard, Ph.D., 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.

### Nima Hejazi

Nima is a Ph.D. candidate in biostatistics with a designated emphasis in computational and genomic biology, working with Mark van der Laan and Alan Hubbard. Nima is affiliated with UC Berkeley’s Center for Computational Biology and is a former NIH Biomedical Big Data fellow. He earned is Master’s in Biostatistics (2017) and a Bachelor’s with a triple major in Molecular and Cell Biology (Neurobiology), Psychology, and Public Health (2015) at UC Berkeley. Nima’s interests span nonparametric estimation, high-dimensional inference, targeted learning, statistical computing, survival analysis, and computational biology, with an emphasis on the development of robust and efficient statistical methodologies that draw on the intersection of causal inference and statistical machine learning. For more information, see https://nimahejazi.org.

### Ivana Malenica

Ivana is a Ph.D. 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 is a Ph.D. student in biostatistics, advised by Alan Hubbard and Mark van der Laan. She has an M.A. in Biostatistics, B.S. in Biology with a Chemistry minor and a B.A. in Mathematics with a Spanish minor. Rachael’s research focuses on narrowing the gap between the theory and application of modern statistics for real-world data science. Specifically, Rachael is motivated by issues arising in healthcare, and she leverages strategies rooted in causal inference and nonparametric estimation to build clinician-tailored, machine-driven solutions. Rachael is also passionate about free, online-mediated education and its corresponding pedagogy.