Targeted Learning in Data Science: Causal Inference for Complex Longitudinal Studies (Springer Series in Statistics)

Mark J. van der Laan, Sherri Rose

  • 出版商: Springer
  • 出版日期: 2018-04-10
  • 售價: $4,640
  • 貴賓價: 9.5$4,408
  • 語言: 英文
  • 頁數: 640
  • 裝訂: Hardcover
  • ISBN: 3319653032
  • ISBN-13: 9783319653037
  • 相關分類: 機率統計學 Probability-and-statisticsData Science
  • 海外代購書籍(需單獨結帳)
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商品描述

This textbook for graduate students in statistics, data science, and public health deals with the practical challenges that come with big, complex, and dynamic data. It presents a scientific roadmap to translate real-world data science applications into formal statistical estimation problems by using the general template of targeted maximum likelihood estimators. These targeted machine learning algorithms estimate quantities of interest while still providing valid inference. Targeted learning methods within data science area critical component for solving scientific problems in the modern age. The techniques can answer complex questions including optimal rules for assigning treatment based on longitudinal data with time-dependent confounding, as well as other estimands in dependent data structures, such as networks. Included in Targeted Learning in Data Science are demonstrations with soft ware packages and real data sets that present a case that targeted learning is crucial for the next generation of statisticians and data scientists. Th is book is a sequel to the first textbook on machine learning for causal inference, Targeted Learning, published in 2011.

Mark van der Laan, PhD, is Jiann-Ping Hsu/Karl E. Peace Professor of Biostatistics and Statistics at UC Berkeley. His research interests include statistical methods in genomics, survival analysis, censored data, machine learning, semiparametric models, causal inference, and targeted learning. Dr. van der Laan received the 2004 Mortimer Spiegelman Award, the 2005 Van Dantzig Award, the 2005 COPSS Snedecor Award, the 2005 COPSS Presidential Award, and has graduated over 40 PhD students in biostatistics and statistics.

Sherri Rose, PhD, is Associate Professor of Health Care Policy (Biostatistics) at Harvard Medical School. Her work is centered on developing and integrating innovative statistical approaches to advance human health. Dr. Rose’s methodological research focuses on nonparametric machine learning for causal inference and prediction. She co-leads the Health Policy Data Science Lab and currently serves as an associate editor for the Journal of the American Statistical Association and Biostatistics.

商品描述(中文翻譯)

這本教科書針對統計學、資料科學和公共衛生的研究生,探討處理大型、複雜和動態數據所面臨的實際挑戰。它提供了一個科學路線圖,將現實世界的數據科學應用轉化為正式的統計估計問題,使用目標最大概似估計器的通用模板。這些目標機器學習算法在估計感興趣的數量時,仍然提供有效的推論。在數據科學領域中,目標學習方法是解決現代科學問題的關鍵組成部分。這些技術可以回答複雜問題,包括基於長期依賴性數據的時間相關混淆的最佳處理規則,以及依賴性數據結構(如網絡)中的其他估計量。《目標學習在數據科學中》中包含了軟件包和真實數據集的演示,展示了目標學習對下一代統計學家和數據科學家的重要性。這本書是2011年出版的關於因果推斷的機器學習的第一本教科書《目標學習》的續集。

Mark van der Laan, PhD是加州大學伯克利分校的Jiann-Ping Hsu/Karl E. Peace生物統計學和統計學教授。他的研究興趣包括基因統計方法、生存分析、被審查數據、機器學習、半參數模型、因果推斷和目標學習。van der Laan博士獲得了2004年Mortimer Spiegelman獎、2005年Van Dantzig獎、2005年COPSS Snedecor獎、2005年COPSS總統獎,並指導了40多名生物統計學和統計學的博士生。

Sherri Rose, PhD是哈佛醫學院的醫療保健政策(生物統計學)副教授。她的工作集中在開發和整合創新的統計方法,以促進人類健康。Rose博士的方法論研究集中在非參數機器學習用於因果推斷和預測。她共同領導健康政策數據科學實驗室,目前擔任《美國統計協會期刊》和《生物統計學》的副編輯。