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© 2018

Targeted Learning in Data Science

Causal Inference for Complex Longitudinal Studies

Textbook

Part of the Springer Series in Statistics book series (SSS)

Table of contents

  1. Front Matter
    Pages i-xlii
  2. Targeted Learning in Data Science: Introduction

    1. Front Matter
      Pages 1-1
    2. Sherri Rose, Mark J. van der Laan
      Pages 3-14
    3. Sherri Rose, Mark J. van der Laan
      Pages 15-25
    4. Sherri Rose, Mark J. van der Laan
      Pages 27-34
    5. Sherri Rose, Mark J. van der Laan
      Pages 35-47
  3. Additional Core Topics

    1. Front Matter
      Pages 49-49
    2. Mark J. van der Laan, Wilson Cai, Susan Gruber
      Pages 51-75
    3. Mark J. van der Laan, David Benkeser
      Pages 77-94
    4. Mark J. van der Laan
      Pages 95-102
    5. Mark J. van der Laan
      Pages 103-123
    6. Alan E. Hubbard, Chris J. Kennedy, Mark J. van der Laan
      Pages 125-142
    7. Mark J. van der Laan, Antoine Chambaz, Cheng Ju
      Pages 143-161
  4. Randomized Trials

    1. Front Matter
      Pages 163-163
    2. David Benkeser, Marco Carone, Peter Gilbert
      Pages 165-174
    3. Laura B. Balzer, Maya L. Petersen, Mark J. van der Laan
      Pages 175-193
    4. Laura B. Balzer, Mark J. van der Laan, Maya L. Petersen
      Pages 195-215
  5. Observational Data

    1. Front Matter
      Pages 217-217
    2. Iván Díaz, Mark J. van der Laan
      Pages 219-232
    3. Mireille E. Schnitzer, Mark J. van der Laan, Erica E. M. Moodie, Robert W. Platt
      Pages 233-251

About this book

Introduction

This textbook for graduate students in statistics, data science, and public health deals with the practical challenges that come with big, complex, and dynamic data. It presents a scientific roadmap to translate real-world data science applications into formal statistical estimation problems by using the general template of targeted maximum likelihood estimators. These targeted machine learning algorithms estimate quantities of interest while still providing valid inference. Targeted learning methods within data science area critical component for solving scientific problems in the modern age. The techniques can answer complex questions including optimal rules for assigning treatment based on longitudinal data with time-dependent confounding, as well as other estimands in dependent data structures, such as networks. Included in Targeted Learning in Data Science are demonstrations with soft ware packages and real data sets that present a case that targeted learning is crucial for the next generation of statisticians and data scientists. Th is book is a sequel to the first textbook on machine learning for causal inference, Targeted Learning, published in 2011.

Mark van der Laan, PhD, is Jiann-Ping Hsu/Karl E. Peace Professor of Biostatistics and Statistics at UC Berkeley. His research interests include statistical methods in genomics, survival analysis, censored data, machine learning, semiparametric models, causal inference, and targeted learning. Dr. van der Laan received the 2004 Mortimer Spiegelman Award, the 2005 Van Dantzig Award, the 2005 COPSS Snedecor Award, the 2005 COPSS Presidential Award, and has graduated over 40 PhD students in biostatistics and statistics.

Sherri Rose, PhD, is Associate Professor of Health Care Policy (Biostatistics) at Harvard Medical School. Her work is centered on developing and integrating innovative statistical approaches to advance human health. Dr. Rose’s methodological research focuses on nonparametric machine learning for causal inference and prediction. She co-leads the Health Policy Data Science Lab and currently serves as an associate editor for the Journal of the American Statistical Association and Biostatistics.

Keywords

targeted minimum loss estimation targeted learning longitudinal data big data precision medicine targeted maximum likelihood estimation applied statistics causal inference super learning data science dependent data

Authors and affiliations

  1. 1.Division of Biostatistics and Department of StatisticsUniversity of California, BerkeleyBerkeleyUSA
  2. 2.Department of Health Care PolicyHarvard Medical SchoolBostonUSA

About the authors

Mark van der Laan, PhD, is Jiann-Ping Hsu/Karl E. Peace Professor of Biostatistics and Statistics at UC Berkeley. His research interests include statistical methods in genomics, survival analysis, censored data, machine learning, semiparametric models, causal inference, and targeted learning. His applied research involves applications in HIV and safety analysis, among others. He has published over 250 journal articles, 4 books, and one handbook on big data. Dr. van der Laan is also co-founder and co-editor of the International Journal of Biostatistics and the Journal of Causal Inference and associate editor of a variety of journals. Dr. van der Laan received the 2004 Mortimer Spiegelman Award, the 2005 Van Dantzig Award, the 2005 COPSS Snedecor Award, the 2005 COPSS Presidential Award, and has graduated over 40 PhD students in biostatistics or statistics.  

Sherri Rose, PhD, is Associate Professor of Health Care Policy (Biostatistics) at Harvard Medical School. Her work is centered on developing and integrating innovative statistical approaches to advance human health. Dr. Rose’s methodological research focuses on nonparametric machine learning for causal inference and prediction. She has made major contributions to the development and application of targeted learning estimators, as well as adaptations to super learning for varied scientific problems. Within health policy, Dr. Rose works on comparative effectiveness research, health program impact evaluation, and computational health economics. She co-leads the Health Policy Data Science Lab and currently serves as an associate editor for the Journal of the American Statistical Association and Biostatistics

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Reviews

“A list of abbreviations, including all the statistical terms used in the textbook, as well as a list of tables and figures would be a welcome addition to the book. This may be particularly useful as the TMLE is a very important application in parametric statistics, and may be used by biostatisticians … . Specifically, those with a very good knowledge of advanced theoretical statistics, including the observational and modeling statistics that are almost prerequisite for appreciating this textbook.” (Ramzi El Feghali, ISCB News, iscb.info, Issue 67, June, 2019)


“The book recommends itself as a thorough overview of TMLE approaches with a variety of examples and case studies, all presented in detail, in a text-book like manner, making this work accessible to a wide audience from undergraduates to established researchers.” (Irina Ioana Mohorianu, zbMATH 1408.62005, 2019)