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عنوان
Fundamentals of causal inference

پدید آورنده
by Babette A. Brumback.,Brumback, Babette A.,

موضوع
Estimation theory.,Conditional expectations (Mathematics),Effect sizes (Statistics),Acyclic models.,Causation,Inference,R (Computer program language),Mathematical models.,Mathematical models.

رده
QA276
.
8
.
B78
2022

کتابخانه
Library of College of Science University of Tehran

محل استقرار
استان: Tehran ـ شهر: Tehran

Library of College of Science University of Tehran

تماس با کتابخانه : 61112616-66495290-021

INTERNATIONAL STANDARD BOOK NUMBER

(Number (ISBN
9780367705053
(Number (ISBN
9780367705053
(Number (ISBN
9781003146674

NATIONAL BIBLIOGRAPHY NUMBER

Number
E3440

LANGUAGE OF THE ITEM

.Language of Text, Soundtrack etc
انگلیسی

TITLE AND STATEMENT OF RESPONSIBILITY

Title Proper
Fundamentals of causal inference
Other Title Information
with R
First Statement of Responsibility
by Babette A. Brumback.

.PUBLICATION, DISTRIBUTION, ETC

Place of Publication, Distribution, etc.
Boca Raton
Name of Publisher, Distributor, etc.
CRC Press
Date of Publication, Distribution, etc.
2022.

PHYSICAL DESCRIPTION

Specific Material Designation and Extent of Item
xii, 236 p.

SERIES

Series Title
(Texts in statistical science)

INTERNAL BIBLIOGRAPHIES/INDEXES NOTE

Text of Note
Includes index.

CONTENTS NOTE

Text of Note
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.
0

SUMMARY OR ABSTRACT

Text of Note
"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"--

TOPICAL NAME USED AS SUBJECT

Entry Element
Estimation theory.
Entry Element
Conditional expectations (Mathematics)
Entry Element
Effect sizes (Statistics)
Entry Element
Acyclic models.
Entry Element
Causation
Entry Element
Inference
Entry Element
R (Computer program language)
Topical Subdivision
Mathematical models.
Topical Subdivision
Mathematical models.

DEWEY DECIMAL CLASSIFICATION

Edition
23

LIBRARY OF CONGRESS CLASSIFICATION

Class number
QA276
.
8
Book number
.
B78
2022

PERSONAL NAME - PRIMARY RESPONSIBILITY

Entry Element
Brumback, Babette A.,

ORIGINATING SOURCE

Country
ایران
Agency
University of Tehran. Library of College of Science
Date of Transaction
20210923174038.0
Cataloguing Rules (Descriptive Conventions))
rda

ELECTRONIC LOCATION AND ACCESS

Date and Hour of Consultation and Access
UT_SCI_BL_DB_1003725_0001.pdf

e

BL
278840

a
Y

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