Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction

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9780521885881: Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction

Most questions in social and biomedical sciences are causal in nature: what would happen to individuals, or to groups, if part of their environment were changed? In this groundbreaking text, two world-renowned experts present statistical methods for studying such questions. This book starts with the notion of potential outcomes, each corresponding to the outcome that would be realized if a subject were exposed to a particular treatment or regime. In this approach, causal effects are comparisons of such potential outcomes. The fundamental problem of causal inference is that we can only observe one of the potential outcomes for a particular subject. The authors discuss how randomized experiments allow us to assess causal effects and then turn to observational studies. They lay out the assumptions needed for causal inference and describe the leading analysis methods, including, matching, propensity-score methods, and instrumental variables. Many detailed applications are included, with special focus on practical aspects for the empirical researcher.

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Book Description:

Many applied research questions are fundamentally questions of causality: Is a new drug effective? Does a training program affect someone's chances of finding a job? What is the effect of a new regulation on economic activity? In this ground-breaking text, two world-renowned experts present statistical methods for studying such questions.

About the Author:

Guido W. Imbens is Professor of Economics at the Graduate School of Business, Stanford University. He has held tenured faculty positions at Harvard University, the University of California, Los Angeles, the University of California, Berkeley, and Stanford University. He is a fellow of the Econometric Society and the American Academy of Arts and Sciences. Imbens has published widely in economics and statistics journals, including Econometrica, The American Economic Review, the Annals of Statistics, the Journal of the American Statistical Association, Biometrika, and the Journal of the Royal Statistical Society.

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Descripción CAMBRIDGE UNIVERSITY PRESS, United Kingdom, 2015. Hardback. Estado de conservación: New. Language: English . Brand New Book. Most questions in social and biomedical sciences are causal in nature: what would happen to individuals, or to groups, if part of their environment were changed? In this groundbreaking text, two world-renowned experts present statistical methods for studying such questions. This book starts with the notion of potential outcomes, each corresponding to the outcome that would be realized if a subject were exposed to a particular treatment or regime. In this approach, causal effects are comparisons of such potential outcomes. The fundamental problem of causal inference is that we can only observe one of the potential outcomes for a particular subject. The authors discuss how randomized experiments allow us to assess causal effects and then turn to observational studies. They lay out the assumptions needed for causal inference and describe the leading analysis methods, including matching, propensity-score methods, and instrumental variables. Many detailed applications are included, with special focus on practical aspects for the empirical researcher. Nº de ref. de la librería AAA9780521885881

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Descripción CAMBRIDGE UNIVERSITY PRESS, United Kingdom, 2015. Hardback. Estado de conservación: New. Language: English . Brand New Book. Most questions in social and biomedical sciences are causal in nature: what would happen to individuals, or to groups, if part of their environment were changed? In this groundbreaking text, two world-renowned experts present statistical methods for studying such questions. This book starts with the notion of potential outcomes, each corresponding to the outcome that would be realized if a subject were exposed to a particular treatment or regime. In this approach, causal effects are comparisons of such potential outcomes. The fundamental problem of causal inference is that we can only observe one of the potential outcomes for a particular subject. The authors discuss how randomized experiments allow us to assess causal effects and then turn to observational studies. They lay out the assumptions needed for causal inference and describe the leading analysis methods, including matching, propensity-score methods, and instrumental variables. Many detailed applications are included, with special focus on practical aspects for the empirical researcher. Nº de ref. de la librería AAA9780521885881

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Descripción Cambridge University Press 2015, 2015. Estado de conservación: New. New hardback. May show some slight shelf wear but content fine and unread. Nº de ref. de la librería A169786

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Descripción Cambridge University Press, 2015. Estado de conservación: New. This text presents statistical methods for studying causal effects and discusses how readers can assess such effects in simple randomized experiments. Num Pages: 644 pages, 18 b/w illus. 97 tables. BIC Classification: PBT. Category: (P) Professional & Vocational. Dimension: 188 x 262 x 36. Weight in Grams: 1292. . 2015. 1st Edition. Hardcover. . . . . . Nº de ref. de la librería V9780521885881

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