Targeted Learning in Data Science : Causal Inference for Complex Longitudinal Studies. 1st ed. 2018

種類:

電子ブック

責任表示:

by Mark J. van der Laan, Sherri Rose

出版情報:

Cham : Springer International Publishing : Imprint: Springer, 2018

著者名:

• van der Laan, Mark J.
• Rose, Sherri.
• SpringerLink (Online service)

シリーズ名:

Springer Series in Statistics ;

ISBN:

9783319653044 [3319653040]    

注記:

Abbreviations and Notation -- Philosophy of Targeted Learning in Data Science -- Part I: Introductory Chapters -- 1. The Statistical Estimation Problem in Complex Longitudinal Big Data -- 2. Longitudinal Causal Models -- 3. Super Learner for Longitudinal Problems -- 4. Longitudinal Targeted Maximum Likelihood Estimation (LTMLE) -- 5. Understanding LTMLE -- 6. Why LTMLE? -- Part II:Additional Core Topics -- 7. One-Step TMLE -- IV: Observational Longitudinal Data -- 19. Super Learning in the ICU -- 20. Stochastic Single-Time-Point Interventions -- 21. Stochastic Multiple-Time-Point Interventions on Monitoring and Treatment -- 22. Collaborative LTMLE -- Part V: Optimal Dynamic Regimes -- 23. Targeted Adaptive Designs Learning the Optimal Dynamic Treatment -- 24. Targeted Learning of the Optimal Dynamic Treatment -- 25. Optimal Dynamic Treatments under Resource Constraints -- Part VI: Computing -- 26. ltmle() for R -- 27. Scaled Super Learner for R -- 28. Scaling CTMLE for Julia -- Part VII: Special Topics.-29. D
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 statist

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