Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Practical Approaches to Causal Relationship Exploration

Practical Approaches to Causal Relationship Exploration

Li, Jiuyong
Liu, Lin
Le, Thuc Duy

Paperback

Series: Springerbriefs in Electrical and Computer Engineering

DatabasesGeneral ComputersProbability & Statistics

ISBN10: 3319144324
ISBN13: 9783319144320
Publisher: Springer
Published: Mar 25 2015
Pages: 80
Weight: 0.31
Height: 0.19 Width: 6.14 Depth: 9.21
Language: English
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.

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