Propensity Score Analysis

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Opis: Propensity Score Analysis - Mark Fraser, Shenyang Guo

With a strong focus on practical applications, the authors explore various strategies for employing PSA. In addition, they discuss the use of PSA with alternative types of data and limitations of PSA under a variety of constraints. Unlike the existing textbooks on program evaluation and causal inference, Propensity Score Analysis delves into statistical concepts, formulas, and models in the context of a robust and engaging focus on application. Over the past 35 years, methods of program evaluation have undergone a significant change, and the researchers have recognized the need to develop more efficient approaches for assessing treatment effects from studies based on observational data and for evaluations based on quasi-experimental designs. Written by experts, this volume is updated and fully reflects the current changes to the field. It offers a systematic review of the history, origins, and statistical foundations of propensity score analysis, and more. -- NeoPopRealism JournalList of Tables List of Figures Preface About the Authors Chapter 1: Introduction Observational Studies History and Development Randomized Experiments Why and When a Propensity Score Analysis Is Needed Computing Software Packages Plan of the Book Chapter 2: Counterfactual Framework and Assumptions Causality, Internal Validity, and Threats Counterfactuals and the Neyman-Rubin Counterfactual Framework The Ignorable Treatment Assignment Assumption The Stable Unit Treatment Value Assumption Methods for Estimating Treatment Effects The Underlying Logic of Statistical Inference Types of Treatment Effects Treatment Effect Heterogeneity Heckman's Econometric Model of Causality Conclusion Chapter 3: Conventional Methods for Data Balancing Why Is Data Balancing Necessary? A Heuristic Example Three Methods for Data Balancing Design of the Data Simulation Results of the Data Simulation Implications of the Data Simulation Key Issues Regarding the Application of OLS Regression Conclusion Chapter 4: Sample Selection and Related Models The Sample Selection Model Treatment Effect Model Overview of the Stata Programs and Main Features of treatreg Examples Conclusion Chapter 5: Propensity Score Matching and Related Models Overview The Problem of Dimensionality and the Properties of Propensity Scores Estimating Propensity Scores Matching Postmatching Analysis Propensity Score Matching With Multilevel Data Overview of the Stata and R Programs Examples Conclusion Chapter 6: Propensity Score Subclassification Overview The Overlap Assumption and Methods to Address Its Violation Structural Equation Modeling With Propensity Score Subclassification The Stratification-Multilevel Method Examples Conclusion Chapter 7: Propensity Score Weighting Overview Weighting Estimators Examples Conclusion Chapter 8: Matching Estimators Overview Methods of Matching Estimators Overview of the Stata Program nnmatch Examples Conclusion Chapter 9: Propensity Score Analysis With Nonparametric Regression Overview Methods of Propensity Score Analysis With Nonparametric Regression Overview of the Stata Programs psmatch2 and bootstrap Examples Conclusion Chapter 10: Propensity Score Analysis of Categorical or Continuous Treatments Overview Modeling Doses With a Single Scalar Balancing Score Estimated by an Ordered Logistic Regression Modeling Doses With Multiple Balancing Scores Estimated by a Multinomial Logit Model The Generalized Propensity Score Estimator Overview of the Stata gpscore Program Examples Conclusion Chapter 11: Selection Bias and Sensitivity Analysis Selection Bias: An Overview A Monte Carlo Study Comparing Corrective Models Rosenbaum's Sensitivity Analysis Overview of the Stata Program rbounds Examples Conclusion Chapter 12: Concluding Remarks Common Pitfalls in Observational Studies: A Checklist for Critical Review Approximating Experiments With Propensity Score Approaches Other Advances in Modeling Causality Directions for Future Development References Index


Szczegóły: Propensity Score Analysis - Mark Fraser, Shenyang Guo

Tytuł: Propensity Score Analysis
Autor: Mark Fraser, Shenyang Guo
Producent: SAGE Publications Ltd
ISBN: 9781452235004
Rok produkcji: 2014
Ilość stron: 448
Oprawa: Twarda
Waga: 0.89 kg


Recenzje: Propensity Score Analysis - Mark Fraser, Shenyang Guo

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Propensity Score Analysis

,

With a strong focus on practical applications, the authors explore various strategies for employing PSA. In addition, they discuss the use of PSA with alternative types of data and limitations of PSA under a variety of constraints. Unlike the existing textbooks on program evaluation and causal inference, Propensity Score Analysis delves into statistical concepts, formulas, and models in the context of a robust and engaging focus on application. Over the past 35 years, methods of program evaluation have undergone a significant change, and the researchers have recognized the need to develop more efficient approaches for assessing treatment effects from studies based on observational data and for evaluations based on quasi-experimental designs. Written by experts, this volume is updated and fully reflects the current changes to the field. It offers a systematic review of the history, origins, and statistical foundations of propensity score analysis, and more. -- NeoPopRealism JournalList of Tables List of Figures Preface About the Authors Chapter 1: Introduction Observational Studies History and Development Randomized Experiments Why and When a Propensity Score Analysis Is Needed Computing Software Packages Plan of the Book Chapter 2: Counterfactual Framework and Assumptions Causality, Internal Validity, and Threats Counterfactuals and the Neyman-Rubin Counterfactual Framework The Ignorable Treatment Assignment Assumption The Stable Unit Treatment Value Assumption Methods for Estimating Treatment Effects The Underlying Logic of Statistical Inference Types of Treatment Effects Treatment Effect Heterogeneity Heckman's Econometric Model of Causality Conclusion Chapter 3: Conventional Methods for Data Balancing Why Is Data Balancing Necessary? A Heuristic Example Three Methods for Data Balancing Design of the Data Simulation Results of the Data Simulation Implications of the Data Simulation Key Issues Regarding the Application of OLS Regression Conclusion Chapter 4: Sample Selection and Related Models The Sample Selection Model Treatment Effect Model Overview of the Stata Programs and Main Features of treatreg Examples Conclusion Chapter 5: Propensity Score Matching and Related Models Overview The Problem of Dimensionality and the Properties of Propensity Scores Estimating Propensity Scores Matching Postmatching Analysis Propensity Score Matching With Multilevel Data Overview of the Stata and R Programs Examples Conclusion Chapter 6: Propensity Score Subclassification Overview The Overlap Assumption and Methods to Address Its Violation Structural Equation Modeling With Propensity Score Subclassification The Stratification-Multilevel Method Examples Conclusion Chapter 7: Propensity Score Weighting Overview Weighting Estimators Examples Conclusion Chapter 8: Matching Estimators Overview Methods of Matching Estimators Overview of the Stata Program nnmatch Examples Conclusion Chapter 9: Propensity Score Analysis With Nonparametric Regression Overview Methods of Propensity Score Analysis With Nonparametric Regression Overview of the Stata Programs psmatch2 and bootstrap Examples Conclusion Chapter 10: Propensity Score Analysis of Categorical or Continuous Treatments Overview Modeling Doses With a Single Scalar Balancing Score Estimated by an Ordered Logistic Regression Modeling Doses With Multiple Balancing Scores Estimated by a Multinomial Logit Model The Generalized Propensity Score Estimator Overview of the Stata gpscore Program Examples Conclusion Chapter 11: Selection Bias and Sensitivity Analysis Selection Bias: An Overview A Monte Carlo Study Comparing Corrective Models Rosenbaum's Sensitivity Analysis Overview of the Stata Program rbounds Examples Conclusion Chapter 12: Concluding Remarks Common Pitfalls in Observational Studies: A Checklist for Critical Review Approximating Experiments With Propensity Score Approaches Other Advances in Modeling Causality Directions for Future Development References Index

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Cena 301,00 PLN
Nasza cena 281,44 PLN
Oszczędzasz 6%
Wysyłka: Niedostępna
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Szczegóły: Propensity Score Analysis - Mark Fraser, Shenyang Guo

Tytuł: Propensity Score Analysis
Autor: Mark Fraser, Shenyang Guo
Producent: SAGE Publications Ltd
ISBN: 9781452235004
Rok produkcji: 2014
Ilość stron: 448
Oprawa: Twarda
Waga: 0.89 kg


Recenzje: Propensity Score Analysis - Mark Fraser, Shenyang Guo

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