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Targeted Learning in Data Science - Causal Inference for Complex Longitudinal Studies (Hardcover, 1st ed. 2018)
Loot Price: R3,286
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Targeted Learning in Data Science - Causal Inference for Complex Longitudinal Studies (Hardcover, 1st ed. 2018)
Series: Springer Series in Statistics
Expected to ship within 10 - 15 working days
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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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