Authors:
- 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
Part of the book series: Springer Series in Statistics (SSS)
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Table of contents (30 chapters)
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Front Matter
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Targeted Learning in Data Science: Introduction
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Front Matter
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Observational Data
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Front Matter
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About this book
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 integratinginnovative 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.
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)
Authors and Affiliations
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Division of Biostatistics and Department of Statistics, University of California, Berkeley, Berkeley, USA
Mark J. van der Laan
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Department of Health Care Policy, Harvard Medical School, Boston, USA
Sherri Rose
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.
Bibliographic Information
Book Title: Targeted Learning in Data Science
Book Subtitle: Causal Inference for Complex Longitudinal Studies
Authors: Mark J. van der Laan, Sherri Rose
Series Title: Springer Series in Statistics
DOI: https://doi.org/10.1007/978-3-319-65304-4
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Springer International Publishing AG, part of Springer Nature 2018
Hardcover ISBN: 978-3-319-65303-7Published: 10 April 2018
Softcover ISBN: 978-3-030-09736-3Published: 15 December 2018
eBook ISBN: 978-3-319-65304-4Published: 28 March 2018
Series ISSN: 0172-7397
Series E-ISSN: 2197-568X
Edition Number: 1
Number of Pages: XLII, 640
Number of Illustrations: 37 b/w illustrations
Topics: Statistical Theory and Methods, Big Data/Analytics, Statistics for Life Sciences, Medicine, Health Sciences, Biostatistics, Biomedical Engineering/Biotechnology, Public Health
Industry Sectors: Aerospace, Biotechnology, Chemical Manufacturing, Energy, Utilities & Environment, Finance, Business & Banking, IT & Software, Oil, Gas & Geosciences, Pharma, Telecommunications