9783319653037-3319653032-Targeted Learning in Data Science: Causal Inference for Complex Longitudinal Studies (Springer Series in Statistics)

Targeted Learning in Data Science: Causal Inference for Complex Longitudinal Studies (Springer Series in Statistics)

ISBN-13: 9783319653037
ISBN-10: 3319653032
Edition: 1st ed. 2018
Author: Mark J. van der Laan, Sherri Rose
Publication date: 2018
Publisher: Springer
Format: Hardcover 682 pages
Category: Statistics
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Book details

ISBN-13: 9783319653037
ISBN-10: 3319653032
Edition: 1st ed. 2018
Author: Mark J. van der Laan, Sherri Rose
Publication date: 2018
Publisher: Springer
Format: Hardcover 682 pages
Category: Statistics

Summary

Acknowledged authors Mark J. van der Laan , Sherri Rose wrote Targeted Learning in Data Science: Causal Inference for Complex Longitudinal Studies (Springer Series in Statistics) comprising 682 pages back in 2018. Textbook and eTextbook are published under ISBN 3319653032 and 9783319653037. Since then Targeted Learning in Data Science: Causal Inference for Complex Longitudinal Studies (Springer Series in Statistics) textbook was available to sell back to BooksRun online for the top buyback price or rent at the marketplace.

Description

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.

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