新书求助：Propensity Score Analysis: Statistical Methods and Applications,2nd

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收藏 2015-05-26

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Propensity Score Analysis: Statistical Methods and Applications, Second Edition
英文原书第一版和基于第一版的中文翻译版论坛内都有坛友上传了。

Authors:	Shenyang Y. Guo and Mark W. Fraser
Publisher:	Sage
Copyright:	2014
ISBN-13:	978-1-4522-3500-4
Pages:	448; hardcover
Price:	$69.50

封面：

＝＝＝Description ＝＝＝
Fully updated to reflect the most recent changes in the field, the Second Edition of Propensity Score Analysis provides an accessible, systematic review of the origins, history, and statistical foundations of propensity score analysis, illustrating how it can be used for solving evaluation and causal-inference problems. With a strong focus on practical applications, the authors explore various strategies for employing PSA, discuss the use of PSA with alternative types of data, and delineate the limitations of PSA under a variety of constraints. Unlike existing textbooks on program evaluation and causal inference, this book delves into statistical concepts, formulas, and models within the context of a robust and engaging focus on application.

The most significant change of the second edition is discussion of propensity score subclassification, propensity score weighting, and dosage analysis from Chapter 5 to separate chapters. These methods are closely related to the Rosenbaum and Rubin’s (1983) seminal study of the development of propensity scores—it is for this reason that Chapter 5 of the first edition pooled these methods together. Because subclassification and weighting methods have been widely applied in recent research and have become recommended models for addressing challenging data issues (Imbens & Wooldridge, 2009), we decided to give each topic a separate treatment. There is an increasing need in social behavioral and health research to model treatment dosage and to extend the propensity score approach from the binary treatment conditions context to categorical and/or continuous treatment conditions contexts. Given these considerations, we treated dosage analysis in the second edition as a separate chapter. As a result, Chapter 5 now focuses on propensity score matching methods alone, including greedy matching and optimal matching.

＝＝＝Features/New to This Edition＝＝＝
NEW TO THIS EDITION:

Propensity score sub-classification and propensity score weighting are treated as separate models to give thorough attention to each.
Newly expanded coverage of analyzing treatment dosage in the context of propensity score modeling broadens the scope of application for readers.
New coverage of modeling heterogeneous treatment effects includes two nonparametric tests and a discussion of modeling issues to ensure students are on the cutting edge.
Expanded content on propensity score analysis with multilevel data includes new discussions of four multilevel models for estimating propensity scores and two strategies for controlling clustering effects in outcome analysis.
The principles and issues related to running propensity score models with sub-classification and weighting are covered in depth.
The authors demonstrate new software and include clear illustrations for analyzing treatment dosage with GPS.

KEY FEATURES:

The authors present key information on model derivations and summarize complex statistical arguments—omitting their proofs to challenge readers to apply their learning.
Each method, and its empirical examples, is linked to specific Stata programs for seamless integration of learning and application.
Two conceptual frameworks—the Neyman-Rubin counterfactual framework and the Heckman econometric model of causality—provide a foundation for understanding key topics.
Examples in every chapter demonstrate real challenges found in social and health sciences research.
Data simulation is used to illustrate key points.
New statistical approaches necessary for understanding the seven evaluation methods are included.