Title: Probabilistic Active Learning of Functions in Structural Causal Models,
Abstract: We consider the problem of learning the functions computing children from
parents in a Structural Causal Model once the underlying causal graph has been
identified. This is in some sense the second step after causal discovery.
Taking a probabilistic approach to estimating these functions, we derive a
natural myopic active learning scheme that identifies the intervention which is
optimally informative about all of the unknown functions jointly, given
previously observed data. We test the derived algorithms on simple examples, to
demonstrate that they produce a structured exploration policy that
significantly improves on unstructured base-lines.