Date Added: Jul 2010
Causal knowledge is frequently pursued by researchers in many fields, such as medicine, economics, and social science, yet very little research in knowledge discovery focuses on discovering causal knowledge. Those researchers rely on a set of methods, called experimental and quasi-experimental designs, that exploit the ontological structure of the world to limit the set of possible statistical models that can produce observed correlations among variables. As a result, designs are powerful techniques for drawing conclusions about cause-and-effect relationships. However, designs are almost never used explicitly by knowledge discovery algorithms. In this paper, the authors provide explicit evidence that designs have the potential to be highly useful as part of algorithms to discover causal knowledge.