Practical Approaches to Causal Relationship Exploration

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 caus...

Πλήρης περιγραφή

Λεπτομέρειες βιβλιογραφικής εγγραφής
Κύριοι συγγραφείς: Li, Jiuyong (Συγγραφέας), Liu, Lin (Συγγραφέας), Le, Thuc Duy (Συγγραφέας)
Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
Γλώσσα:English
Έκδοση: Cham : Springer International Publishing : Imprint: Springer, 2015.
Σειρά:SpringerBriefs in Electrical and Computer Engineering,
Θέματα:
Διαθέσιμο Online:Full Text via HEAL-Link
LEADER 03081nam a22005055i 4500
001 978-3-319-14433-7
003 DE-He213
005 20150302115850.0
007 cr nn 008mamaa
008 150302s2015 gw | s |||| 0|eng d
020 |a 9783319144337  |9 978-3-319-14433-7 
024 7 |a 10.1007/978-3-319-14433-7  |2 doi 
040 |d GrThAP 
050 4 |a Q334-342 
050 4 |a TJ210.2-211.495 
072 7 |a UYQ  |2 bicssc 
072 7 |a TJFM1  |2 bicssc 
072 7 |a COM004000  |2 bisacsh 
082 0 4 |a 006.3  |2 23 
100 1 |a Li, Jiuyong.  |e author. 
245 1 0 |a Practical Approaches to Causal Relationship Exploration  |h [electronic resource] /  |c by Jiuyong Li, Lin Liu, Thuc Duy Le. 
264 1 |a Cham :  |b Springer International Publishing :  |b Imprint: Springer,  |c 2015. 
300 |a X, 80 p. 55 illus.  |b online resource. 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
347 |a text file  |b PDF  |2 rda 
490 1 |a SpringerBriefs in Electrical and Computer Engineering,  |x 2191-8112 
505 0 |a 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. 
520 |a 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. 
650 0 |a Computer science. 
650 0 |a Data mining. 
650 0 |a Artificial intelligence. 
650 1 4 |a Computer Science. 
650 2 4 |a Artificial Intelligence (incl. Robotics). 
650 2 4 |a Data Mining and Knowledge Discovery. 
700 1 |a Liu, Lin.  |e author. 
700 1 |a Le, Thuc Duy.  |e author. 
710 2 |a SpringerLink (Online service) 
773 0 |t Springer eBooks 
776 0 8 |i Printed edition:  |z 9783319144320 
830 0 |a SpringerBriefs in Electrical and Computer Engineering,  |x 2191-8112 
856 4 0 |u http://dx.doi.org/10.1007/978-3-319-14433-7  |z Full Text via HEAL-Link 
912 |a ZDB-2-SCS 
950 |a Computer Science (Springer-11645)