Targeted Learning: Causal Inference for Observational and Experimental Data (Springer Series in Statistics)
ISBN-13:
9781441997814
ISBN-10:
1441997814
Edition:
2011
Author:
Mark J. van der Laan, Sherri Rose
Publication date:
2011
Publisher:
Springer
Format:
Hardcover
700 pages
Category:
Biology
,
Biological Sciences
FREE US shipping
Book details
ISBN-13:
9781441997814
ISBN-10:
1441997814
Edition:
2011
Author:
Mark J. van der Laan, Sherri Rose
Publication date:
2011
Publisher:
Springer
Format:
Hardcover
700 pages
Category:
Biology
,
Biological Sciences
Summary
Targeted Learning: Causal Inference for Observational and Experimental Data (Springer Series in Statistics) (ISBN-13: 9781441997814 and ISBN-10: 1441997814), written by authors
Mark J. van der Laan, Sherri Rose, was published by Springer in 2011.
With an overall rating of 3.9 stars, it's a notable title among other
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Description
- Establishes causal inference methodology that incorporates the benefits of machine learning with statistical inference
- Presentation combines accessibility with the method's rigorous grounding in statistical theory
- Demonstrates targeted learning in epidemiological, medical, and genomic experimental and observational studies that include informative dropout, missingness, time-dependent confounding, and case-control sampling
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
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