The impact of prior knowledge on causal structure learning

Understand

Causal Bayesian networks have become a powerful technology for reasoning under uncertainty in areas that require transparency and explainability, by relying on causal assumptions that enable us to simulate hypothetical interventions.

• The graphical structure of such models can be estimated by structure learning algorithms, domain knowledge, or a combination of both.
• Various knowledge approaches have been proposed in the literature that enable us to specify prior knowledge that constrains or guides these algorithms.
• This paper introduces some novel, and also describes some existing, knowledge-based approaches that enable us to combine structure learning with knowledge obtained from heterogeneous sources.