Fundamentals of Causal Inference: With R (Hardcover)

Brumback, Babette A.

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商品描述

One of the primary motivations for clinical trials and observational studies of humans is to infer cause and effect. Disentangling causation from confounding is of utmost importance. Fundamentals of Causal Inference explains and relates different methods of confounding adjustment in terms of potential outcomes and graphical models, including standardization, difference-in-differences estimation, the front-door method, instrumental variables estimation, and propensity score methods. It also covers effect-measure modification, precision variables, mediation analyses, and time-dependent confounding. Several real data examples, simulation studies, and analyses using R motivate the methods throughout. The book assumes familiarity with basic statistics and probability, regression, and R and is suitable for seniors or graduate students in statistics, biostatistics, and data science as well as PhD students in a wide variety of other disciplines, including epidemiology, pharmacy, the health sciences, education, and the social, economic, and behavioral sciences.

Beginning with a brief history and a review of essential elements of probability and statistics, a unique feature of the book is its focus on real and simulated datasets with all binary variables to reduce complex methods down to their fundamentals. Calculus is not required, but a willingness to tackle mathematical notation, difficult concepts, and intricate logical arguments is essential. While many real data examples are included, the book also features the Double What-If Study, based on simulated data with known causal mechanisms, in the belief that the methods are best understood in circumstances where they are known to either succeed or fail. Datasets, R code, and solutions to odd-numbered exercises are available at www.routledge.com.

商品描述(中文翻譯)

臨床試驗和人類觀察研究的主要動機之一是推斷因果關係。解開因果關係與混雜因素的關係至關重要。《因果推論基礎》以潛在結果和圖形模型的角度解釋和關聯不同的混雜因素調整方法,包括標準化、差異估計、前門法、儀器變量估計和傾向分數方法。它還涵蓋了效應測量修改、精確變量、中介分析和時間相關混雜因素。書中通過多個真實數據示例、模擬研究和使用R進行分析來推動這些方法。本書假設讀者對基本統計學和概率、回歸和R有一定的了解,適合統計學、生物統計學和數據科學的高年級本科生或研究生,以及流行病學、藥學、健康科學、教育學以及社會、經濟和行為科學等其他學科的博士研究生。

書籍從簡要介紹歷史和概率統計的基本要素開始,其獨特之處在於將複雜方法簡化為基礎原理,並使用真實和模擬的二元變量數據集。書中不需要微積分,但需要有處理數學符號、困難概念和複雜邏輯論證的能力。雖然書中包含了許多真實數據示例,但還特別介紹了“雙重假設研究”,該研究基於已知的因果機制使用模擬數據,以便在已知成功或失敗的情況下更好地理解這些方法。數據集、R代碼和奇數習題的解答可在www.routledge.com上獲得。

作者簡介

Babette A. Brumback is Professor and Associate Chair for Education in the Department of Biostatistics at the University of Florida; she won the department's Outstanding Teacher Award for 2020-2021. A Fellow of the American Statistical Association, she has researched and applied methods for causal inference since 1998, specializing in methods for time-dependent confounding, complex survey samples and clustered data.

作者簡介(中文翻譯)

Babette A. Brumback是佛羅里達大學生物統計學系的教授兼教育副主任;她在2020-2021年獲得了該系的傑出教師獎。作為美國統計學會的會士,她自1998年以來一直從事因果推論的研究和應用方法,專注於處理時間相依混淆、複雜調查樣本和集群數據的方法。