Practical Approaches to Causal Relationship Exploration / by Jiuyong Li, Lin Liu, Thuc Duy Le.

Format:

Book

Author/Creator:

Li, Jiuyong, author.

Liu, Lin (Computer engineer), author.

Le, Thuc Duy, author.

Contributor:

SpringerLink (Online service)

Series:

Computer Science (Springer-11645)

SpringerBriefs in electrical and computer engineering 2191-8112

SpringerBriefs in Electrical and Computer Engineering, 2191-8112

Language:

English

Subjects (All):

Artificial intelligence.

Data mining.

Artificial Intelligence.

Data Mining and Knowledge Discovery.

Local Subjects:

Artificial Intelligence.

Data Mining and Knowledge Discovery.

Physical Description:

1 online resource (X, 80 pages) : 55 illustrations.

Edition:

First edition 2015.

Contained In:

Springer eBooks

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2015.

System Details:

text file PDF

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.

Contents:

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.

Other Format:

Printed edition:

ISBN:

978-3-319-14433-7

9783319144337

Access Restriction:

Restricted for use by site license.