Targeted Learning in Data Science: Causal Inference for Complex Longitudinal Studies

Name: Targeted Learning in Data Science: Causal Inference for Complex Longitudinal Studies
Brand: Springer
SKU: 9783319653037
Price: 8732.0 INR
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by Mark J. Van Der Laan

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ISBN13: 9783319653037
Binding: Hardcover
Subject: N/A
Publisher: Springer
Publisher Imprint: Springer
Publication Date: April 10, 2018
Pages: 632
Original Price: EUR 119.99
Language: English
Edition: 2018
Item Weight: 1125 grams
BISAC Subject(s): Probability & Statistics / General, Industries / Computers & Information Technology, and Biostatistics

About The Book

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

About The Author

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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