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Fundamentals of causal inference (Record no. 565213)

MARC details
000 -LEADER
fixed length control field 02618 a2200265 4500
003 - CONTROL NUMBER IDENTIFIER
control field OSt
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20250422160150.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 220121b xxu||||| |||| 00| 0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 9780367705053
040 ## - CATALOGING SOURCE
Transcribing agency IIT Kanpur
041 ## - LANGUAGE CODE
Language code of text/sound track or separate title eng
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 519.50285
Item number B834f
100 ## - MAIN ENTRY--AUTHOR NAME
Personal name Brumback, Babette A.
245 ## - TITLE STATEMENT
Title Fundamentals of causal inference
Remainder of title with R
Statement of responsibility, etc Babette A. Brumback
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Name of publisher CRC Press
Year of publication 2022
Place of publication Boca Raton, FL
300 ## - PHYSICAL DESCRIPTION
Number of Pages xii,236p
440 ## - SERIES STATEMENT/ADDED ENTRY--TITLE
Title Chapman and Hall/CRC texts in statistical science series
490 ## - SERIES STATEMENT
Series statement /edited by Joseph K. Blitzstein ...[et al.]
500 ## - GENERAL NOTE
General note Includes bibliographical references and index
520 ## - SUMMARY, ETC.
Summary, etc 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.<br/><br/>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.
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical Term Mathematical statistics -- Data processing
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical Term Causation
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical Term Inference
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type Books
Holdings
Withdrawn status Lost status Damaged status Not for loan Collection code Home library Current library Date acquired Source of acquisition Cost, normal purchase price Full call number Accession Number Cost, replacement price Koha item type
        General Stacks PK Kelkar Library, IIT Kanpur PK Kelkar Library, IIT Kanpur 31/01/2022 60 4332.16 519.50285 B834f A185526 5415.20 Books

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