Propensity score analysis

Propensity score analysis: statistical methods and applications

by Guo, Shenyang

Type Book
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Fully updated to reflect the most recent changes in the field, this Second Edition 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 …

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Title Statements

  • Full title :Propensity score analysis : statistical methods and applications / Shenyang Guo, ... Mark W. Fraser, ...

Summary

Fully updated to reflect the most recent changes in the field, this Second Edition 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.

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Edition Statements

  • Publication Date:cop. 2015.
  • Edition:2nd edition

Publication Statements

  • Publisher(s):SAGE Publications
  • Place of Publication:Thousand Oaks, Calif
  • Language:eng
  • Publication Date:cop. 2015.

The Physical Object

  • Description:1 vol. (xxiv-421 p.) : : ill. ; : 24 cm.

Edition Identifiers

  • ISBN:9781452235004, 1452235007
  • oclc:ocn957662295
  • LC Classification:HA29
  • Dewey Decimal:519.5/3

Table of Contents

Introduction -- Counterfactual framework and assumptions -- Conventional methods for data balancing -- Sample selection and related models -- Propensity score matching and related models -- Propensity score subclassification -- Propensity score weighting -- Matching estimators -- Propensity score analysis with nonparametric regression -- Propensity score analysis of categorical or continuous treatments -- Selection bias and sensitivity analysis -- Concluding remarks.

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