| 书目名称 | Targeted Learning |
| 副标题 | Causal Inference for |
| 编辑 | Mark J. van der Laan,Sherri Rose |
| 视频video | |
| 概述 | Establishes causal inference methodology that incorporates the benefits of machine learning with statistical inference.Presentation combines accessibility with the method‘s rigorous grounding in stati |
| 丛书名称 | Springer Series in Statistics |
| 图书封面 |  |
| 描述 | .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, |
| 出版日期 | Book 2011 |
| 关键词 | Causal inference; High-dimensional and complex data; Nonparametric and semiparametric statistics; Obser |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-1-4419-9782-1 |
| isbn_softcover | 978-1-4614-2911-1 |
| isbn_ebook | 978-1-4419-9782-1Series ISSN 0172-7397 Series E-ISSN 2197-568X |
| issn_series | 0172-7397 |
| copyright | Springer Science+Business Media, LLC 2011 |
发表于 2025-3-21 17:59:39
