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

This textbook provides a scientific roadmap to translate real-world data science applications into formal statistical estimation problems, using targeted maximum likelihood estimators. It demonstrates the importance of targeted learning methods in solving complex questions, such as optimal rules for assigning treatment based on longitudinal data with time-dependent confounding.

\n Format: Hardback
\n Length: 640 pages
\n Publication date: 10 April 2018
\n Publisher: Springer International Publishing AG
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This comprehensive textbook caters to graduate students studying statistics, data science, and public health, addressing the practical complexities associated with large, intricate, and dynamic data. It provides a scientific framework to transform real-world data science applications into formal statistical estimation problems, utilizing the general template of targeted maximum likelihood estimators. These targeted machine learning algorithms not only estimate quantities of interest but also ensure valid inference. Targeted learning methodologies have become an indispensable component in the field of data science, enabling the solution of complex scientific challenges in the modern era. The techniques showcased in this book can address intricate questions such as developing optimal rules for assigning treatments based on longitudinal data with time-dependent confounding, as well as other estimands in dependent data structures like networks.

Targeted Learning in Data Science offers practical demonstrations using software packages and real data sets to illustrate the critical importance of targeted learning for the next generation of statisticians and data scientists. Authored by Mark van der Laan, Ph.D., the Jiann-Ping Hsu/Karl E. Peace Professor of Biostatistics and Statistics at UC Berkeley, this book serves as a sequel to his earlier work, Targeted Learning, published in 2011.

Dr. van der Laan's research encompasses a wide range of statistical methodologies, including genomics, survival analysis, censored data, machine learning, semiparametric models, causal inference, and targeted learning. He has received numerous prestigious awards, including the 2004 Mortimer Spiegelman Award, the 2005 Van Dantzig Award, the 2005 COPSS Snedecor Award, the 2005 COPSS Presidential Award, and has mentored over 40 Ph.D. students in biostatistics and statistics.

Joining him as an author is Sherri Rose, Ph.D., Associate Professor of Health Care Policy (Biostatistics) at Harvard Medical School. Together, they have authored a textbook that empowers graduate students with the knowledge and tools necessary to navigate the challenges of working with big, complex, and dynamic data, paving the way for groundbreaking discoveries and advancements in various fields.

\n Weight: 1104g\n
Dimension: 234 x 158 x 37 (mm)\n
ISBN-13: 9783319653037\n
Edition number: 1st ed. 2018\n