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Practical Approaches to Causal Relationship Exploration

  • Book
  • © 2015

Overview

Authors:
  1. Jiuyong Li

    1. University of South Australia, Adelaide, Australia

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    You can also search for this author in PubMed   Google Scholar

  2. Lin Liu

    1. School of Information Technology and Mathematical Sciences, University of South Australia, Adelaide, Australia

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    You can also search for this author in PubMed   Google Scholar

  3. Thuc Duy Le

    1. School of Information Technology and Mathematical Sciences, University of South Australia, Adelaide, Australia

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    You can also search for this author in PubMed   Google Scholar

Part of the book series: SpringerBriefs in Electrical and Computer Engineering (BRIEFSELECTRIC)

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Table of contents (6 chapters)

  1. Front Matter

    Pages i-x
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  2. Introduction

    • Jiuyong Li, Lin Liu, Thuc Duy Le
    Pages 1-7

  3. Local Causal Discovery with a Simple PC Algorithm

    • Jiuyong Li, Lin Liu, Thuc Duy Le
    Pages 9-21

  4. A Local Causal Discovery Algorithm for High Dimensional Data

    • Jiuyong Li, Lin Liu, Thuc Duy Le
    Pages 23-32

  5. Causal Rule Discovery with Partial Association Test

    • Jiuyong Li, Lin Liu, Thuc Duy Le
    Pages 33-50

  6. Causal Rule Discovery with Cohort Studies

    • Jiuyong Li, Lin Liu, Thuc Duy Le
    Pages 51-66

  7. Experimental Comparison and Discussions

    • Jiuyong Li, Lin Liu, Thuc Duy Le
    Pages 67-72

  8. Back Matter

    Pages 73-80
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Keywords

About this book

This brief presents four practical methods to effectively explore causal relationships, which are often used for explanation, prediction and decision making in medicine, epidemiology, biology, economics, physics and social sciences. The first two methods apply conditional independence tests for causal discovery. The last two methods employ association rule mining for efficient causal hypothesis generation, and a partial association test and retrospective cohort study for validating the hypotheses. All four methods are innovative and effective in identifying potential causal relationships around a given target, and each has its own strength and weakness. For each method, a software tool is provided along with examples demonstrating its use. Practical Approaches to Causal Relationship Exploration is designed for researchers and practitioners working in the areas of artificial intelligence, machine learning, data mining, and biomedical research. The material also benefits advanced students interested in causal relationship discovery.

Authors and Affiliations

  • University of South Australia, Adelaide, Australia

    Jiuyong Li

  • School of Information Technology and Mathematical Sciences, University of South Australia, Adelaide, Australia

    Lin Liu, Thuc Duy Le

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