Affiliations: Department of Computer Science, Katholieke
Universiteit Leuven, Celestijnenlaan 200A, 3001 Leuven, Belgium. {wannes.meert,
jan.struyf, hendrik.blockeel}@cs.kuleuven.be
Note: [] Address for correspondence: Department of Computer Science,
Katholieke Universiteit Leuven, Celestijnenlaan 200A, 3001 Leuven, Belgium
Abstract: Causal relations are present in many application domains. Causal
Probabilistic Logic (CP-logic) is a probabilistic modeling language that is
especially designed to express such relations. This paper investigates the
learning of CP-logic theories (CP-theories) from training data. Its first
contribution is SEM-CP-logic, an algorithm that learns CP-theories by
leveraging Bayesian network (BN) learning techniques. SEM-CP-logic is based on
a transformation between CP-theories and BNs. That is, the method applies BN
learning techniques to learn a CP-theory in the form of an equivalent BN. To
this end, certain modifications are required to the BN parameter learning and
structure search, the most important one being that the refinement operator
used by the search must guarantee that the constructed BNs represent valid
CP-theories. The paper's second contribution is a theoretical and
experimental comparison between CP-theory and BN learning. We show that the
most simple CP-theories can be represented with BNs consisting of noisy-OR
nodes, while more complex theories require close to fully connected networks
(unless additional unobserved nodes are introduced in the network). Experiments
in a controlled artificial domain show that in the latter cases CP-theory
learning with SEM-CP-logic requires fewer training data than BN learning. We
also apply SEM-CP-logic in a medical application in the context of HIV
research, and show that it can compete with state-of-the-art methods in this
domain.
