TARGETED LEARNING IN DATA SCIENCE

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