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Practical approaches to causal relat...

Le, Thuc Duy.

Practical approaches to causal relationship exploration

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Practical approaches to causal relationship explorationby Jiuyong Li, Lin Liu, Thuc Duy Le.
作者:	Li, Jiuyong.
其他作者:	Liu, Lin.
出版者:	Cham :Springer International Publishing :2015.
面頁冊數:	x, 80 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
標題:	Data mining.
電子資源:	http://dx.doi.org/10.1007/978-3-319-14433-7
ISBN:	9783319144337 (electronic bk.)

Practical approaches to causal relationship exploration
Li, Jiuyong.

Practical approaches to causal relationship exploration[electronic resource] /by Jiuyong Li, Lin Liu, Thuc Duy Le. - Cham :Springer International Publishing :2015. - x, 80 p. :ill., digital ;24 cm. - SpringerBriefs in electrical and computer engineering,2191-8112. - SpringerBriefs in electrical and computer engineering..

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.

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.

ISBN: 9783319144337 (electronic bk.)

Standard No.: 10.1007/978-3-319-14433-7doiSubjects--Topical Terms:

184440
Data mining.

LC Class. No.: QA76.9.D343

Dewey Class. No.: 006.312

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