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

Mark J. Van Der Laan

ISBN 10: 3319653032 / ISBN 13: 9783319653037

Edité par Springer, Springer, 2018

A propos de cet article

Druck auf Anfrage Neuware - Printed after ordering - This textbook for graduate students in statistics, data science, and public health dealswith the practical challenges that come with big, complex, and dynamic data. It presentsa scientific roadmap to translate real-world data science applications into formal statisticalestimation problems by using the general template of targeted maximum likelihoodestimators. These targeted machine learning algorithms estimate quantities of interestwhile still providing valid inference. Targeted learning methods within data science area critical component for solving scientific problems in the modern age. The techniquescan answer complex questions including optimal rules for assigning treatment basedon longitudinal data with time-dependent confounding, as well as other estimands independent data structures, such as networks. Included in Targeted Learning in DataScience are demonstrations with soft ware packages and real data sets that present acase that targeted learning is crucial for the next generation of statisticians and datascientists. Th is book is a sequel to the first textbook on machine learning for causalinference, Targeted Learning, published in 2011.Mark van der Laan, PhD, is Jiann-Ping Hsu/Karl E. Peace Professor of Biostatistics andStatistics at UC Berkeley. His research interests include statistical methods in genomics,survival analysis, censored data, machine learning, semiparametric models, causalinference, and targeted learning. Dr. van der Laan received the 2004 Mortimer SpiegelmanAward, the 2005 Van Dantzig Award, the 2005 COPSS Snedecor Award, the 2005COPSS Presidential Award, and has graduated over 40 PhD students in biostatisticsand statistics.Sherri Rose, PhD, is Associate Professor of Health Care Policy (Biostatistics) at HarvardMedical School. Her work is centered on developing and integratinginnovative statisticalapproaches to advance human health. Dr. Rose's methodological research focuseson nonparametric machine learning for causal inference and prediction. She co-leadsthe Health Policy Data Science Lab and currently serves as an associate editor for theJournal of the American Statistical Association and Biostatistics.

N° de réf. du vendeur 9783319653037

Détails bibliographiques

• Titre: Targeted Learning in Data Science : Causal Inference for Complex Longitudinal Studies
• Éditeur: Springer, Springer
• Date de parution: 2018
• Langue: anglais
• ISBN à 10 chiffres: 3319653032
• ISBN à 13 chiffres: 9783319653037
• Reliure: Buch
• État de l'article: Neu
• Dimensions: 241 L x 160 H x 42 P (en millimètres) 241 millimètres de largeur par 160 millimètres de hauteur par 42 millimètres de profondeur
• Poids: 1180 grammes

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Synopsis

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

À propos de l?auteur

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.