Statistical Causal Inferences and Their Applications in Public Health Research

Statistical Causal Inferences and Their Applications in Public Health Research edited by Hua He, Pan Wu, Ding-Geng (Din) Chen.

This book compiles and presents new developments in statistical causal inference. The accompanying data and computer programs are publicly available so readers may replicate the model development and data analysis presented in each chapter. In this way, methodology is taught so that readers may impl...

Full description

Saved in:
Bibliographic Details
Corporate Author: SpringerLink (Online service)
Other Authors: He, Hua (Editor), Wu, Pan (Editor), Chen, Ding-Geng (Din) (Editor)
Format: eBook
Language:English
Published: Cham : Springer International Publishing : Imprint: Springer, 2016.
Edition:1st ed. 2016.
Series:ICSA Book Series in Statistics,
Springer eBook Collection.
Subjects:
Statistics .
Biostatistics.
Public health.
Electronic resources (E-books)
Online Access:Click to view e-book
Holy Cross Note:Loaded electronically.
Electronic access restricted to members of the Holy Cross Community.
Table of Contents:
  • Part I. Overview
  • 1. Causal Inference – A Statistical Paradigm for Inferring Causality
  • Part II. Propensity Score Method for Causal Inference
  • 2. Overview of Propensity Score Methods
  • 3. Sufficient Covariate, Propensity Variable and Doubly Robust Estimation
  • 4. A Robustness Index of Propensity Score Estimation to Uncontrolled Confounders
  • 5. Missing Confounder Data in Propensity Score Methods for Causal Inference
  • 6. Propensity Score Modeling & Evaluation
  • 7. Overcoming the Computing Barriers in Statistical Causal Inference
  • Part III. Causal Inference in Randomized Clinical Studies
  • 8. Semiparametric Theory and Empirical Processes in Causal Inference
  • 9. Structural Nested Models for Cluster-Randomized Trials
  • 10. Causal Models for Randomized Trials with Continuous Compliance
  • 11. Causal Ensembles for Evaluating the Effect of Delayed Switch to Second-line Antiretroviral Regimens
  • 12. Structural Functional Response Models for Complex Intervention Trials
  • Part IV. Structural Equation Models for Mediation Analysis
  • 13.Identification of Causal Mediation Models with An Unobserved Pre-treatment Confounder
  • 14. A Comparison of Potential Outcome Approaches for Assessing Causal Mediation
  • 15. Causal Mediation Analysis Using Structure Equation Models. .

Similar Items