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Targeted Learning in Data Science

Causal Inference for Complex Longitudinal Studies

  • Mark J. van der Laan
  • Sherri Rose

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
    4. Romain S. Neugebauer, Julie A. Schmittdiel, Patrick J. O’Connor, Mark J. van der Laan
      Pages 253-276
    5. Wenjing Zheng, Mark J. van der Laan
      Pages 277-299
  6. Online Learning

    1. Front Matter
      Pages 301-301
    2. Mark J. van der Laan, David Benkeser
      Pages 303-315
    3. Mark J. van der Laan, Antoine Chambaz, Sam Lendle
      Pages 317-346
  7. Networks

    1. Front Matter
      Pages 347-347
    2. Oleg Sofrygin, Mark J. van der Laan
      Pages 349-371
    3. Oleg Sofrygin, Elizabeth L. Ogburn, Mark J. van der Laan
      Pages 373-396
  8. Optimal Dynamic Rules

    1. Front Matter
      Pages 397-397
    2. Alexander R. Luedtke, Mark J. van der Laan
      Pages 399-417
    3. Alexander R. Luedtke, Mark J. van der Laan
      Pages 419-435
    4. Antoine Chambaz, Wenjing Zheng, Mark J. van der Laan
      Pages 437-451
  9. Special Topics

    1. Front Matter
      Pages 453-453
    2. Mark J. van der Laan, Aurélien Bibaut, Alexander R. Luedtke
      Pages 455-481
    3. Marco Carone, Iván Díaz, Mark J. van der Laan
      Pages 483-510
    4. Iván Díaz, Alexander R. Luedtke, Mark J. van der Laan
      Pages 511-522
    5. Jeremy Coyle, Mark J. van der Laan
      Pages 523-539
    6. Antoine Chambaz, Emilien Joly, Xavier Mary
      Pages 541-559
  10. Back Matter
    Pages 585-640

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

  • Mark J. van der Laan
    • 1
  • Sherri Rose
    • 2
  1. 1.Division of Biostatistics and Department of StatisticsUniversity of California, BerkeleyBerkeleyUSA
  2. 2.Department of Health Care PolicyHarvard Medical SchoolBostonUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-65304-4
  • Copyright Information Springer International Publishing AG, part of Springer Nature 2018
  • Publisher Name Springer, Cham
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-3-319-65303-7
  • Online ISBN 978-3-319-65304-4
  • Series Print ISSN 0172-7397
  • Series Online ISSN 2197-568X
  • Buy this book on publisher's site
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