Fundamentals of Causal Inference, 1st Edition

Confirm Banking institution For Principles of Causal Inference, 1st Edition

1. ISBN-10

   0367705052
2. ISBN-13

   978-0367705053

3. “Entirely, this textbook is an excellent resource for researchers and students who aim to comprehend the foundation and methods of causal inference. Each chapter provides an R implementation of the introduced causal concepts and models and concludes with appropriate exercises.”-An-Shun Tai & Sheng-Hsuan Lin, in Biometrics

   One of the main incentives for clinical trials and observational studies of individuals is to deduce cause and effect. Untangling causation from confounding is of utmost significance. Principles of Causal Inference explains and connects various techniques 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 analysis, and analyses using R inspire the techniques 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 range of other disciplines, including epidemiology, pharmacy, the health sciences, education, and the social, economic, and behavioral sciences.

   Commencing 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 simplify complex methods to their fundamentals. Calculus is not required, but a willingness to grapple with mathematical notation, challenging concepts, and intricate logical arguments is essential. While many real data examples are included, the book also features the Double What-If Analysis, based on simulated data with known causal mechanisms, under the belief that the methods are best understood in situations where they are known to either succeed or fail. Datasets, R code, and solutions to odd-numbered exercises are available on the book’s

   Babette A. Brumback is Professor and Associate Chair for Education in the Department of Biostatistics at the University of Florida; she received 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, focusing on methods for time-dependent confounding, complex survey samples and clustered data.