Targeted Learning - Causal Inference for Observational and Experimental Data (Paperback, 2011 ed.)

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.

General

Imprint:	Springer-Verlag New York
Country of origin:	United States
Series:	Springer Series in Statistics
Release date:	August 2013
First published:	2011
Authors:	Mark J.Van Der Laan • Sherri Rose
Dimensions:	235 x 155 x 43mm (L x W x T)
Format:	Paperback
Pages:	628
Edition:	2011 ed.
ISBN-13:	978-1-4614-2911-1
Categories:	Books > Science & Mathematics > Mathematics > Probability & statistics Books > Medicine > General issues > Public health & preventive medicine > General
LSN:	1-4614-2911-0
Barcode:	9781461429111

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