Targeted Learning in Data Science

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.

“A list of abbreviations, including all the statistical terms used in the textbook, as well as a list of tables and figures would be a welcome addition to the book. This may be particularly useful as the TMLE is a very important application in parametric statistics, and may be used by biostatisticians … . Specifically, those with a very good knowledge of advanced theoretical statistics, including the observational and modeling statistics that are almost prerequisite for appreciating this textbook.” (Ramzi El Feghali,ISCB News, iscb.info, Issue 67, June, 2019)“The book recommends itself as a thorough overview of TMLE approaches with a variety of examples and case studies, all presented in detail, in a text-book like manner, making this work accessible to a wide audience from undergraduates to established researchers.” (Irina Ioana Mohorianu, zbMATH 1408.62005, 2019)

• 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. Data-Adaptive Target Parameters.- 30. Double Robust Inference for LTMLE.- 31. Higher-Order TMLE.- Appendix.- A. Online Targeted Learning Theory.- B. Computerization of the calculation of efficient influence curve.- C. TMLE applied to Capture/Recapture.- D. TMLE for High Dimensional Linear Regression.- E. TMLE of Causal Effect Based on Observing a Single Time Series.