Statistical Causal Inferences and Their Applications in Public Health Research

Part I. Overview

1. Causal Inference: A Statistical Paradigm for Inferring Causality
Pan Wu, Wan Tang, Tian Chen, Hua He, Douglas Gunzler, Xin M. Tu

Part II. Propensity Score Method for Causal Inference

2. Overview of Propensity Score Methods
Hua He, Jun Hu, Jiang He

3. Sufficient Covariate, Propensity Variable and Doubly Robust Estimation
Hui Guo, Philip Dawid, Giovanni Berzuini

4. A Robustness Index of Propensity Score Estimation to Uncontrolled Confounders
Wei Pan, Haiyan Bai

5. Missing Confounder Data in Propensity Score Methods for Causal Inference
Bo Fu, Li Su

6. Propensity Score Modeling and Evaluation
Yeying Zhu, Lin (Laura) Lin

7. Overcoming the Computing Barriers in Statistical Causal Inference
Kai Zhang, Ding-Geng Chen

Part III. Causal Inference in Randomized Clinical Studies

8. Semiparametric Theory and Empirical Processes in Causal Inference
Edward H. Kennedy

9. Structural Nested Models for Cluster-Randomized Trials
Shanjun Helian, Babette A. Brumback, Matthew C. Freeman, Richard Rheingans

10. Causal Models for Randomized Trials with Continuous Compliance
Yan Ma, Jason Roy

11. Causal Ensembles for Evaluating the Effect of Delayed Switch to Second-Line Antiretroviral Regimens
Li Li, Brent A. Johnson

12. Structural Functional Response Models for Complex Intervention Trials
Pan Wu, Xin M. Tu

Part IV. Structural Equation Models for Mediation Analysis

13. Identification of Causal Mediation Models with an Unobserved Pre-treatment Confounder
Ping He, Zhenguo Wu, Xiaohua Douglas Zhang, Zhi Geng

14. A Comparison of Potential Outcome Approaches for Assessing Causal Mediation
Donna L. Coffman, David P. MacKinnon, Yeying Zhu, Debashis Ghosh

15. Causal Mediation Analysis Using Structure Equation Models
Douglas Gunzler, Nathan Morris, Xin M. Tu

Avainsanat: Statistics, Statistics for Life Sciences, Medicine, Health Sciences, Biostatistics, Public Health