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Statistical Causal Inferences and Their Applications in Public Health Research

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Cover of 'Statistical Causal Inferences and Their Applications in Public Health Research'

Table of Contents

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    Book Overview
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    Chapter 1 Causal Inference: A Statistical Paradigm for Inferring Causality
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    Chapter 2 Overview of Propensity Score Methods
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    Chapter 3 Sufficient Covariate, Propensity Variable and Doubly Robust Estimation
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    Chapter 4 A Robustness Index of Propensity Score Estimation to Uncontrolled Confounders
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    Chapter 5 Missing Confounder Data in Propensity Score Methods for Causal Inference
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    Chapter 6 Propensity Score Modeling and Evaluation
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    Chapter 7 Overcoming the Computing Barriers in Statistical Causal Inference
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    Chapter 8 Semiparametric Theory and Empirical Processes in Causal Inference
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    Chapter 9 Structural Nested Models for Cluster-Randomized Trials
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    Chapter 10 Causal Models for Randomized Trials with Continuous Compliance
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    Chapter 11 Causal Ensembles for Evaluating the Effect of Delayed Switch to Second-Line Antiretroviral Regimens
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    Chapter 12 Structural Functional Response Models for Complex Intervention Trials
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    Chapter 13 Identification of Causal Mediation Models with an Unobserved Pre-treatment Confounder
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    Chapter 14 A Comparison of Potential Outcome Approaches for Assessing Causal Mediation
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    Chapter 15 Causal Mediation Analysis Using Structure Equation Models
Attention for Chapter 6: Propensity Score Modeling and Evaluation
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Chapter title
Propensity Score Modeling and Evaluation
Chapter number 6
Book title
Statistical Causal Inferences and Their Applications in Public Health Research
Published by
Springer, Cham, January 2016
DOI 10.1007/978-3-319-41259-7_6
Book ISBNs
978-3-31-941257-3, 978-3-31-941259-7
Authors

Yeying Zhu, Lin (Laura) Lin

View on publisher site
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 3 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 3 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 1 33%
Unknown 2 67%
Readers by discipline Count As %
Mathematics 1 33%
Unknown 2 67%

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