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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 8: Semiparametric Theory and Empirical Processes in Causal Inference
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Readers on

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99 Mendeley
Summary
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Chapter title
Semiparametric Theory and Empirical Processes in Causal Inference
Chapter number 8
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_8
Book ISBNs
978-3-31-941257-3, 978-3-31-941259-7
Authors

Edward H. Kennedy

View on publisher site
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 99 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 1%
Unknown 98 99%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 35 35%
Researcher 10 10%
Student > Master 8 8%
Student > Doctoral Student 7 7%
Student > Bachelor 5 5%
Other 12 12%
Unknown 22 22%
Readers by discipline Count As %
Mathematics 27 27%
Economics, Econometrics and Finance 11 11%
Computer Science 8 8%
Engineering 5 5%
Social Sciences 5 5%
Other 16 16%
Unknown 27 27%

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