Probabilistic Active Learning of Functions in Structural Causal Models

Understand

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.