Statistical Causal Inferences and Their Applications in Public Health Research

  • Hua He
  • Pan Wu
  • Ding-Geng (Din) Chen

Part of the ICSA Book Series in Statistics book series (ICSABSS)

Table of contents

  1. Front Matter
    Pages i-xv
  2. Overview

    1. Front Matter
      Pages 1-1
    2. Pan Wu, Wan Tang, Tian Chen, Hua He, Douglas Gunzler, Xin M. Tu
      Pages 3-25
  3. Propensity Score Method for Causal Inference

    1. Front Matter
      Pages 27-27
    2. Hua He, Jun Hu, Jiang He
      Pages 29-48
    3. Hui Guo, Philip Dawid, Giovanni Berzuini
      Pages 49-89
    4. Yeying Zhu, Lin (Laura) Lin
      Pages 111-124
  4. Causal Inference in Randomized Clinical Studies

    1. Front Matter
      Pages 139-139
    2. Shanjun Helian, Babette A. Brumback, Matthew C. Freeman, Richard Rheingans
      Pages 169-186
  5. Structural Equation Models for Mediation Analysis

    1. Front Matter
      Pages 239-239
    2. Ping He, Zhenguo Wu, Xiaohua Douglas Zhang, Zhi Geng
      Pages 241-262
    3. Donna L. Coffman, David P. MacKinnon, Yeying Zhu, Debashis Ghosh
      Pages 263-293
    4. Douglas Gunzler, Nathan Morris, Xin M. Tu
      Pages 295-314
  6. Back Matter
    Pages 315-321

About this book


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 implement it directly. The book brings together experts engaged in causal inference research to present and discuss recent issues in causal inference methodological development. This is also a timely look at causal inference applied to scenarios that range from clinical trials to mediation and public health research more broadly. In an academic setting, this book will serve as a reference and guide to a course in causal inference at the graduate level (Master's or Doctorate). It is particularly relevant for students pursuing degrees in Statistics, Biostatistics and Computational Biology. Researchers and data analysts in public health and biomedical research will also find this book to be an important reference.


causal inference causal inference methodological development causal models for randomized trials causal parameters causal reasoning data analysis for public health data model development propensity score modeling

Editors and affiliations

  • Hua He
    • 1
  • Pan Wu
    • 2
  • Ding-Geng (Din) Chen
    • 3
  1. 1.Department of Epidemiology School of Public Health and Tropical MedicineTulane UniversityNew OrleansUSA
  2. 2.Christiana Care Health SystemValue InstituteNewarkUSA
  3. 3.School of Social Work and Department of BiostatisticsUniversity of North CarolinaChapel HillUSA

Bibliographic information

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