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Springer Series in Statistics

Targeted Learning in Data Science

Causal Inference for Complex Longitudinal Studies

Authors: van der Laan, Mark J., Rose, Sherri

  • Provides essential data analysis tools for answering complex big data questions based on real world data
  • Contains machine learning estimators that provide inference within data science 
  • Offers applications that demonstrate 1) the translation of the real world application into a statistical estimation problem and 2) the targeted statistical learning methodology to answer scientific questions of interest based on real data
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Buy this book

eBook $74.99
price for USA (gross)
  • Customers within the U.S. and Canada please contact Customer Service at 1-800-777-4643, Latin America please contact us at +1-212-460-1500 (Weekdays 8:30am – 5:30pm ET) to place your order.
  • Due: November 22, 2017
  • ISBN 978-3-319-65304-4
  • Digitally watermarked, DRM-free
  • Included format:
  • ebooks can be used on all reading devices
Hardcover $99.00
price for USA
  • Customers within the U.S. and Canada please contact Customer Service at 1-800-777-4643, Latin America please contact us at +1-212-460-1500 (Weekdays 8:30am – 5:30pm ET) to place your order.
  • Due: October 25, 2017
  • ISBN 978-3-319-65303-7
  • Free shipping for individuals worldwide
About this Textbook

This textbook for Masters and PhD graduate students in biostatistics, statistics, data science, and epidemiology deals with the practical challenges that come with big, complex, and dynamic data while maintaining a strong theoretical foundation. 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 to construct targeted machine learning algorithms that incorporate the state-of-the-art in machine learning to estimate quantities of interest, while still providing valid inference. Targeted learning methods within data science are a critical component for answering complex statistical questions in the modern age. The techniques can answer complex questions including optimal rules for assigning treatment based on longitudinal data involving time-dependent confounding and censoring as well as other estimands in dependent data structures, such as networks. Standard methods and software tools are not currently equipped for these challenges; however, targeted learning is tailored for these problems found in precision medicine, big data, and data science. Included in Targeted Learning in Data Science are demonstrations with software packages and real data sets that present a case that targeted learning is crucial for the next generation of statisticians and data scientists.

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 Heal

th 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

Buy this book

eBook $74.99
price for USA (gross)
  • Customers within the U.S. and Canada please contact Customer Service at 1-800-777-4643, Latin America please contact us at +1-212-460-1500 (Weekdays 8:30am – 5:30pm ET) to place your order.
  • Due: November 22, 2017
  • ISBN 978-3-319-65304-4
  • Digitally watermarked, DRM-free
  • Included format:
  • ebooks can be used on all reading devices
Hardcover $99.00
price for USA
  • Customers within the U.S. and Canada please contact Customer Service at 1-800-777-4643, Latin America please contact us at +1-212-460-1500 (Weekdays 8:30am – 5:30pm ET) to place your order.
  • Due: October 25, 2017
  • ISBN 978-3-319-65303-7
  • Free shipping for individuals worldwide
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Bibliographic Information

Bibliographic Information
Book Title
Targeted Learning in Data Science
Book Subtitle
Causal Inference for Complex Longitudinal Studies
Authors
Series Title
Springer Series in Statistics
Copyright
2017
Publisher
Springer International Publishing
Copyright Holder
Springer International Publishing AG
eBook ISBN
978-3-319-65304-4
DOI
10.1007/978-3-319-65304-4
Hardcover ISBN
978-3-319-65303-7
Series ISSN
0172-7397
Edition Number
1
Number of Pages
X, 632
Number of Illustrations and Tables
33 b/w illustrations
Topics