Practical approaches to causal relationship exploration / Jiuyong Li, Lin Liu, Thuc Duy Le.
Li, Jiuyong, author.; Liu, Lin, author.; Le, Thuc Duy, author.
2015
QA76.9.D343 L5 2015eb
Available Online

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Title Practical approaches to causal relationship exploration / Jiuyong Li, Lin Liu, Thuc Duy Le.
Author Li, Jiuyong, author.
ISBN 9783319144337 (electronic book)
3319144332 (electronic book)
9783319144320
DOI https://doi.org/10.1007/978-3-319-14433-7
Published Cham : Springer, 2015.
Language English
Description 1 online resource (x, 80 pages) : illustrations.
Item Number 10.1007/978-3-319-14433-7 doi
Call Number QA76.9.D343 L5 2015eb
Dewey Decimal Classification 006.3/12
Summary 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.
Bibliography, etc. Note Includes bibliographical references.
Access Note Access limited to authorized users.
Source of Description Online resource; title from PDF title page (SpringerLink, viewed March 11, 2015).
Added Author Liu, Lin, author.
Le, Thuc Duy, author.
Series SpringerBriefs in electrical and computer engineering.
Available in Other Form Print version: 9783319144320
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Introduction
Local causal discovery with a simple PC algorithm
A local causal discovery algorithm for high dimensional data
Causal rule discovery with partial association test
Causal rule discovery with cohort studies
Experimental comparison and discussions.

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