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Can I read Targeted Learning in Data Science: Causal Inference for Complex Longitudinal Studies (Springer Series in Statistics) on EtoBox?

Targeted Learning in Data Science: Causal Inference for Complex Longitudinal Studies (Springer Series in Statistics) by Mark J. van der Laan, Sherri Rose is a nonfiction available to read on EtoBox.

What is Targeted Learning in Data Science: Causal Inference for Complex Longitudinal Studies (Springer Series in Statistics) about?

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

Who reads Targeted Learning in Data Science: Causal Inference for Complex Longitudinal Studies (Springer Series in Statistics)?

It is typically read by self-directed learners exploring a subject in depth.

Common subject areas: history, science, philosophy, social sciences.

Author
Mark J. van der Laan, Sherri Rose
Publisher
Springer International Publishing : Imprint : Springer
Published
2018
Language
EN
ISBN
9783319653037
Category
nonfiction
Subjects
Management, Medical, Mathematics

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