Learning Causal Trees from Dependence Information

Dan Geiger, Azaria Paz, Judea Pearl

In constructing probabilistic networks from human judgments, we use causal relationships to convey useful patterns of dependencies. The converse task, that of inferring causal relationships from patterns of dependencies, is far less understood. This paper establishes conditions under which the directionality of some interactions can be determined from non-temporal probabilistic information - an essential prerequisite for attributing a causal interpretation to these interactions. An efficient algorithm is developed that, given data generated by an undisclosed causal polytree, recovers the structure of the underlying polytree, as well as the directionality of all its identifiable links.

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