TARGETED LEARNING IN DATA SCIENCE IBD

Sinopse

This textbook for graduate students in statistics, data science, and public health dealsáwith the practical challenges that come with big, complex, and dynamic data. It presentsáa scientific roadmap to translate real-world data science applications into formal statisticaláestimation problems by using the general template of targeted maximum likelihoodáestimators. These targeted machine learning algorithms estimate quantities of interestáwhile still providing valid inference. Targeted learning methods within data science area critical component for solving scientific problems in the modern age. The techniquesácan answer complex questions including optimal rules for assigning treatment basedáon longitudinal data with time-dependent confounding, as well as other estimands inádependent data structures, such as networks. Included in Targeted Learning in DataáScience are demonstrations with soft ware packages and real data sets that present aácase that targeted learning is crucial for the next generation of statisticians and dataáscientists. Th is book is a sequel to the first textbook on machine learning for causaláinference, Targeted Learning, published in 2011.Mark van der Laan, PhD, is Jiann-Ping Hsu/Karl E. Peace Professor of Biostatistics andáStatistics at UC Berkeley. His research interests include statistical methods in genomics,ásurvival analysis, censored data, machine learning, semiparametric models, causaláinference, and targeted learning. Dr. van der Laan received the 2004 Mortimer SpiegelmanáAward, the 2005 Van Dantzig Award, the 2005 COPSS Snedecor Award, the 2005áCOPSS Presidential Award, and has graduated over 40 PhD students in biostatisticsáand statistics.Sherri Rose, PhD, is Associate Professor of Health Care Policy (Biostatistics) at HarvardáMedical School. Her work is centered on developing and integrating innovative statisticaláapproaches to advance human health. Dr. RoseâÇÖs methodological research focusesáon nonparametric machine learning for causal inference and prediction. She co-leadsáthe Health Policy Data Science Lab and currently serves as an associate editor for theáJournal of the American Statistical Association and Biostatistics.