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

Welcome!

This open source, reproducible vignette is for a 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 introduction to the field of Targeted Learning (TL) for statistical and causal inference, and the corresponding tlverse software ecosystem. Emphasis will be placed on super learning (SL) and targeted minimum loss-based estimation (TMLE) for causal effects of single time point interventions. TMLE represents a finite-sample robust, efficient substitution estimation strategy that uses super (ensemble machine) learning to flexibly adjust for confounding while yielding valid statistical inference. We will discuss TMLE for the causal effects of static and dynamic interventions; time permitting, additional topics to be discussed will include estimation of the causal effects of optimal dynamic and stochastic interventions.

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. Her research integrates causal inference, machine learning, and nonparametric statistics to realistically approximate answers to causal questions with statistical confidence. Motivated by issues arising in healthcare, the projects she’s pursued include the development of (i) clinical algorithm frameworks and guidelines; (ii) real-world data analysis methodologies for generating and evaluating real-world evidence; (iii) open-source software, including key contributions to sl3, origami and hal9001 packages; and (iv) biostatistics graduate-level courses and other educational material for targeted learning and causal inference.