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04126aam a22004218i 4500 001 62EE789E83F811ECA43678234CECA4DB 003 SILO 005 20220202011724 008 210911s2022 flu 001 0 eng 010 $a 2021022048 020 $a 0367705052 020 $a 9780367705053 035 $a (OCoLC)1269415774 040 $a LBSOR/DLC $b eng $e rda $c DLC $d OCLCO $d OCLCF $d SILO 042 $a pcc 050 00 $a QA276.8 B78 2022 100 1 $a Brumback, Babette A., $e author. 245 10 $a Fundamentals of causal inference : $b with R / $c by Babette A. Brumback. 250 $a First edition. 263 $a 2111 264 1 $a Boca Raton : $b CRC Press, $c 2022. 300 $a pages cm. 490 0 $a Texts in statistical science 500 $a Includes index. 505 0 $a Conditional probability and expectation -- Potential outcomes and the fundamental problem of causal inference -- Effect-measure modification and causal interaction -- Causal directed acyclic graphs -- Adjusting for confounding : backdoor method via standardization -- Adjusting for confounding : difference-in-differences estimators -- Adjusting for confounding : front-door method -- Adjusting for confounding : instrumental variables -- Adjusting for confounding : propensity-score methods -- Gaining efficiency with precision variables -- Mediation. 520 $a "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. 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"-- $c Provided by publisher. 650 0 $a Estimation theory. 650 0 $a Conditional expectations (Mathematics) 650 0 $a Effect sizes (Statistics) 650 0 $a Acyclic models. 650 0 $a Causation $x Mathematical models. 650 0 $a Inference $x Mathematical models. 650 0 $a R (Computer program language) 941 $a 2 952 $l JMPC081 $d 20230822012620.0 952 $l USUX851 $d 20230302020305.0 956 $a http://locator.silo.lib.ia.us/search.cgi?index_0=id&term_0=62EE789E83F811ECA43678234CECA4DB 994 $a C0 $b IWAInitiate Another SILO Locator Search