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This paper presents a simple, efficient computer-based method fordiscovering causal relationships from databases that containobservational data. Observational data is passively observed, ascontrasted with experimental data. Most of the databases availablefor data mining are observational. There is great potential formining such databases to discover causal relationships. We illustratehow observational data can constrain the causal relationships amongmeasured variables, sometimes to the point that we can conclude thatone variable is causing another variable. The presentation here isbased on a constraint-based approach to causal discovery. A primarypurpose of this paper is to present the constraint-based causaldiscovery method in the simplest possible fashion in order to (1)readily convey the basic ideas that underlie more complexconstraint-based causal discovery techniques, and (2) permitinterested readers to rapidly program and apply the method to theirown databases, as a start toward using more elaborate causaldiscovery algorithms.