vbid/9783319653044

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 integratinginnovative 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.Typham this is the title: Targeted Learning in Data Science Causal Inference for Complex Longitudinal Studies