The statistics profession is at a unique point in history. The need for valid statistical tools is greater than ever; data sets are massive, often measuring hundreds of thousands of measurements for a single subject. The field is ready to move towards clear objective benchmarks under which tools can be evaluated. Targeted learning allows (1) the full generalization and utilization of cross-validation as an estimator selection tool so that the subjective choices made by humans are now made by the machine, and (2) targeting the fitting of the probability distribution of the data toward the target parameter representing the scientific question of interest.
This book is aimed at both statisticians and applied researchers interested in causal inference and general effect estimation for observational and experimental data. Part I is an accessible introduction to super learning and the targeted maximum likelihood estimator, including related concepts necessary to understand and apply these methods. Parts II-IX handle complex data structures and topics applied researchers will immediately recognize from their own research, including time-to-event outcomes, direct and indirect effects, positivity violations, case-control studies, censored data, longitudinal data including time-dependent confounding, and genomic studies.
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Mark J. van der Laan is a Hsu/Peace Professor of Biostatistics and Statistics at the University of California, Berkeley. His research concerns causal inference, prediction, adjusting for missing and censored data, and estimation based on high-dimensional observational and experimental biomedical and genomic data. He is the recipient of the 2005 COPSS Presidents’ and Snedecor Awards, as well as the 2004 Spiegelman Award, and is a Founding Editor for The International Journal of Biostatistics.
Sherri Rose is an NSF Mathematical Sciences Postdoctoral Research Fellow at the Johns Hopkins Bloomberg School of Public Health. She recently completed her Ph.D. in biostatistics at the University of California, Berkeley where her doctoral work was honored with the Evelyn Fix Memorial Prize. Her research interests include causal inference, prediction, and applications in rare diseases.
From the Authors
When we started writing this book we went back and forth debating the level we were trying to target. Should we generate a textbook that was more like an epidemiology text and would be broadly accessible to a greater number of applied readers with less formal statistical training? Should we develop a purely theoretical text that would mostly be of interest to a certain subset of statisticians? Ultimately, we struck a level that is somewhere in between these two extremes. Since there is no other book on targeted learning, we could not escape the inclusion of statistical formalism. However, we also did not want to lose all accessibility for nontheoreticians. This led to a book that begins with six chapters that should be generally readable by most applied researchers familiar with basic statistical concepts and traditional data analysis. That is not to say many topics won’t be new and challenging, but these chapters are peppered with intuition and explanations to help readers along. The book progresses to more challenging topics and data structures, and follows a recognizable pattern via a road map for targeted learning and the general description of each targeted estimator. Thus, applied readers less interested in why it works and more interested in implementation can tease out those parts. Yet, mathematicians and theoretical statisticians will not get bored, as extensive rigor is included in many chapters, as well as a detailed appendix containing proofs and derivations. Lastly, this book is unique in that it also contains generic viagra rating, yet it is not a traditional edited text. As the authors of Targeted Learning, we have spent significant time crafting and reworking each of the contributed chapters to have consistent style, content, format, and notation as well as a familiar road map. This yields a truly cohesive book that reads easily as one text.
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A new one-day course, Targeted Learning: Causal Inference for Observational and Experimental Data, will be presented at the 2012 buy levitra vardenafil (JSM) this summer in San Diego, CA on July 29, 2012. More details will be provided as they are available. See the cheapest levitra professional page for a summary of the three-day Fall short course sponsored by the cheap levitra paypal and the viagra canadian women as well as downloadable slides, code, and an outline for that course.