Practical Approaches to Causal Relationship Exploration - Couverture souple

Li, Jiuyong

Couverture souple

ISBN 10 : 3319144324 ISBN 13 : 9783319144320

Editeur : Springer, 2015

Synopsis

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.

Détails de l'édition

• Éditeur: Springer
• Date d'édition: 2015
• Langue: anglais
• ISBN 10: 3319144324
• ISBN 13: 9783319144320
• Reliure: Broché
• Nombre de pages: 92
• Coordonnées du fabricant: ProductSafety@springernature.com; ProductSafety@springernature.com; ; Poststr. 9; Darmstadt; 64293; Allemagne