Probabilistic Active Learning of Functions in Structural Causal Models

TL;DR

This paper introduces a probabilistic active learning method for efficiently estimating functions in Structural Causal Models after the causal graph has been identified, improving exploration strategies.

Contribution

It proposes a myopic active learning scheme that optimally selects interventions to learn all functions jointly in causal models.

Findings

The algorithm produces a structured exploration policy.

It significantly outperforms unstructured baseline methods.

Tests on simple examples demonstrate improved learning efficiency.

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.