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.