{"product_id":"targeted-learning-causal-inference-for-observational-and-experimental-data-springer-series-in-statistics","title":"Targeted Learning: Causal Inference for Observational and Experimental Data (Springer Series in Statistics)","description":"\u003cp\u003e\u003cstrong\u003eBook info:\u003c\/strong\u003e Targeted Learning: Causal Inference for Observational and Experimental Data (Springer Series in Statistics) (Hardcover, 700 pages) – Springer, 2011. Language: English.\u003c\/p\u003e\n \u003cul\u003e\n\u003cli\u003eEstablishes causal inference methodology that incorporates the benefits of machine learning with statistical inference\u003c\/li\u003e\n\u003cli\u003ePresentation combines accessibility with the method's rigorous grounding in statistical theory\u003c\/li\u003e\n\u003cli\u003eDemonstrates targeted learning in epidemiological, medical, and genomic experimental and observational studies that include informative dropout, missingness, time-dependent confounding, and case-control sampling\u003c\/li\u003e\n\u003c\/ul\u003e 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, and genomic studies.               \"Targeted Learning, by Mark J. van der Laan and Sherri Rose, fills a much needed gap in statistical and causal inference. It protects us from wasting computational, analytical, and data resources on irrelevant aspects of a problem and teaches us how to focus on what is relevant - answering questions that researchers truly care about.\" -Judea Pearl, Computer Science Department, University of California, Los Angeles  \"In summary, this book should be on the shelf of every investigator who conducts observational research and randomized controlled trials. The concepts and methodology are foundational for causal inference and at the same time stay true to what the data at hand can say about the questions that motivate their collection.\" -Ira B. Tager, Division of Epidemiology, University of California, Berkeley  \n        From the Back Cover   \u003cp\u003eThe 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. \u003cbr\u003e \u003cbr\u003eThis 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, and genomic studies.\u003c\/p\u003e\u003cp\u003e\"Targeted Learning, by Mark J. van der Laan and Sherri Rose, fills a much needed gap in statistical and causal inference. It protects us from wasting computational, analytical, and data resources on irrelevant aspects of a problem and teaches us how to focus on what is relevant – answering questions that researchers truly care about.\"\u003cbr\u003e-Judea Pearl, Computer Science Department, University of California, Los Angeles\u003cbr\u003e\u003cbr\u003e\"In summary, this book should be on the shelf of every investigator who conducts observational research and randomized controlled trials. The concepts and methodology are foundational for causal inference and at the same time staytrue to what the data at hand can say about the questions that motivate their collection.\"\u003cbr\u003e-Ira B. Tager, Division of Epidemiology, University of California, Berkeley\u003c\/p\u003e           About the Author   \u003cp\u003eMark 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.\u003cbr\u003e\u003cbr\u003eSherri Rose is currently a PhD candidate in the Division of Biostatistics at the University of California, Berkeley.  Her research interests include causal inference, prediction, and applications in rare diseases. Upon completion of her doctoral degree, she will begin an NSF Mathematical Sciences Postdoctoral Research Fellowship at Johns Hopkins Bloomberg School of Public Health.\u003c\/p\u003e      ","brand":"Mark J. van der Laan, Sherri Rose","offers":[{"title":"Default Title","offer_id":46069990129898,"sku":"9781441997814","price":214.08,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0714\/5301\/6298\/files\/61M1ADLgTeL._SL1500.jpg?v=1781236012","url":"https:\/\/textbookme.store\/products\/targeted-learning-causal-inference-for-observational-and-experimental-data-springer-series-in-statistics","provider":"TextbookMe","version":"1.0","type":"link"}