EAN: 9783319144320

SPRINGERBRIEFS IN ELECTRICAL AND COMPUTER ENGIN...

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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