Description 
xii, 236 pages ; 21 cm 
Contents 
Conditional probability and expectation  Potential outcomes and the fundamental problem of causal inference  Effectmeasure modification and causal interaction  Causal directed acyclic graphs  Adjusting for confounding : backdoor method via standardization  Adjusting for confounding : differenceindifferences estimators  Adjusting for confounding : frontdoor method  Adjusting for confounding : instrumental variables  Adjusting for confounding : propensityscore methods  Gaining efficiency with precision variables  Mediation 
Summary 
"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, differenceindifferences estimation, the frontdoor method, instrumental variables estimation, and propensity score methods. It also covers effectmeasure modification, precision variables, mediation analyses, and timedependent 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 WhatIf 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 oddnumbered exercises are available at www.routledge.com. 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 20202021. A Fellow of the American Statistical Association, she has researched and applied methods for causal inference since 1998, specializing in methods for timedependent confounding, complex survey samples and clustered data" Provided by publisher 
Notes 
Includes index 
Subject 
Estimation theory


Conditional expectations (Mathematics)


Effect sizes (Statistics)


Acyclic models


Causation  Mathematical models


Inference  Mathematical models


R (Computer program language)


Acyclic models


Causation  Mathematical models


Conditional expectations (Mathematics)


Effect sizes (Statistics)


Estimation theory


Inference  Mathematical models


R (Computer program language)

LC no. 
2021022048 
ISBN 
9780367705053 

0367705052 

9780367705053 
